Nodus 3 - UAntwerpen · The ‘Nodus Preferences’ file should be placed together with the Nodus 3.2 application file (i.e. both on the desktop or both in the same folder) or it

Nodus 3.1

Manual

with Nodus 3.2 Appendix

Neuron and network simulation software for Macintosh¨ computers

Copyright © Erik De Schutter, 1995

CopyrightThis manual and the Nodus software described in it are copyrighted with all rights reserved.This manual or the Nodus software may not be copied without written consent of Erik DeSchutter (E.D.S.), except in the normal use of the software or to make a backup copy.Nodus 3 was compiled with MPW Fortran II, Copyright © Absoft Corporation 1991-94.Macintosh is a registered trademark of Apple Computer, Inc.

Limited WarrantyE.D.S. will replace the media on which this software is distributed at no charge if you report anyphysical defect within 90 days of purchase and return the item to be replaced.E.D.S. makes no warranty or representation, either express or implied, with respect to this soft-ware, its quality, performance, merchantability, or fitness for a particular purpose. As a resultthis software is sold “as is”, and you, the purchaser, are assuming the entire risk as to its qualityand performance.In no event will E.D.S. be liable for direct, indirect, special, incidental, or consequentialdamages resulting from any defect in the software or its documentation.The warranty and remedies set forth above are exclusive and in lieu of all others oral or written,express or implied.This manual describes Nodus version 3.1.2. E.D.S. does not guarantee that later versions ofNodus are accurately described by this manual. In the Appendix changes made in Nodus 3.2.1are described.

Information and ServiceFor further information or to report any problem or difficulty with the Nodus 3 software, pleasewrite to:

Dr. E. De SchutterBorn Bunge FoundationUniversity of Antwerp (UIA)Universiteitsplein 1B2610 AntwerpBelgium

Fax 323/820.2541, telex 33646, e-mail [email protected]

ReferencePapers describing Nodus have been published. Please refer to thes papers when publishingresults of modeling with Nodus.

E. De Schutter: Computer software for development and simulation of compartmentalmodels of neurons. Computers in Biology and Medicine 19: 71-81 (1989).

E. De Schutter: A consumer guide to neuronal modeling software. Trends in Neurosciences15: 462-464 (1992).E. De Schutter: Nodus, a user friendly neuron simulator for Macintosh computers. inNeural Systems: Analysis and Modeling, F.H. Eeckman editor, Kluwer Academic,Norwell MA. 113-119 (1993).

The author would appreciate receiving reprints of any paper referring to work with Nodus.

TABLE OF CONTENTS

I. Introduction 5II. Installing Nodus 3.2 7III. Modeling with Nodus 9

Introduction 9The Mathematics of Compartmental Modeling 11Passive Membrane Models 13Nodus Implementation of Compartmental Models 15Excitable Membrane Models 20Simulation of Synapses and Connections 22Integration Methods 24

IV. Nodus Reference 27Nodus Menus 27Nodus Files 28Making New Models 36Making New Simulations 41Selecting Simulation Parameters 47

V. Nodus Menu Commands 55Apple Menu 55File Menu 55Edit Menu 62Simulation Menu 64Network Menu 80Neuron Menu 83Conductance Menu 99

VI. Using the Examples 103Demo Files 103Realistic Models 112

VII. Appendix 115Maxima for Memory Storage (Nodus 3.2) 115Shift and Option Key Menu Modifications 116Bugs and Problems 117Import Formats 118References 120Nodus 3.2 Update 125Nodus ftp site 136

VIII. Index 137

I -6 Introduction

I. INTRODUCTION

There is no Nodus 3.2 manual (yet). This Nodus 3.1 manual has been updated partially todocument changes made in Nodus 3.2. This chapter and the next one have been rewritten.However, most of the differences between Nodus 3.2 and Nodus 3.1 are listed in the 'Nodus3.2 Update' section of the Appendix. A complete rewrite of the manual will be done whenadditional changes have been made to Nodus.Nodus is a powerful, easy to use application for simulation of neuron and small networkmodels. Compartmental models of neurons with voltage dependent ionic conductances des-cribed by Hodgkin-Huxley like equations and with pre- and postsynaptic sites can be created.Nodus runs simulations of these models. Experiments can be performed by injecting differentkinds of currents, by simulating voltage clamps or by blocking ionic currents. Simulations canproduce a wide variety of color graphics and text outputs.This manual contains all the information necessary to use Nodus 3. It assumes that you haveread ‘Macintosh, the owner's guide’ and are familiar with menus, scrolling, editing text andusing the mouse.Read first ‘II. Installing Nodus 3.2’, which describes how to install Nodus 3.2 on your harddisk. An introduction to the theoretical aspects of modeling is presented in ‘III. Modeling withNodus’. ‘IV. Nodus Reference’ describes in depth the Nodus user interface and gives practicalinstructions about how to use Nodus. ‘V. Nodus Menu Commands’ has a complete descriptionof each menu command. ‘VI. Using the Examples’ describes all the example files on the masterdisk and gives step by step instructions for using them. The ‘Appendix’ contains some usefullists and references to the modeling literature.Users with no modeling experience should first read chapter III and consult the modelingliterature. Refer to chapter VI to try out Nodus with the example files and discover the crucialsteps in using Nodus. Every user (including experienced Nodus 2 users) should read chapterIV.The manual uses text attributes to help the user in relating information to the Nodus userinterface. All menu commands and dialog window button names and text box titles areprinted bold. All Nodus file types are printed italic when they are mentioned for the first time ina paragraph. Warnings are underlined. ‘Titles’ of manual chapters and ‘file names’ are quoted.If you want more information on Nodus 3 or if you have any comments or suggestions pleasecontact the author. User feedback helps me to adapt future versions of Nodus better to the needsof neurobiologists.Please do not distribute copies of this program; it contains copyrighted software. If you haveobtained a “free” copy you can still send your name and address to the author, you will be addedto the Nodus mailing list and receive information on updates, etc.

Introduction I -7

I I-8 Setting up Nodus

II. INSTALLING NODUS 3.2

The required hardware is a Macintosh computer with a 68020 or 68030 microprocessor and anumeric coprocessor (FPU) or a 68040 microprocessor having at least 4 megabytes (Mb) ofmemory and running System 6 or 7. You will get optimal performance using System 7. Nodus3.2 requires at least 2.0 Mb free RAM to run, it has been preset to use 2.2 Mb. The size of theapplication heap memory mainly determines how may windows can be shown on the screensimultaneously. Simulation plot windows with color graphics can take up a lot of memory (upto 0.1 Mb). If available memory gets low, Nodus will warn the user and suggest to close somewindows.Nodus 3.2 comes with a ‘Nodus_Preferences’ file. Nodus 3 cannot run without this file.Nodus cannot make a default preferences file so be sure to have a backup! Note that thepreferences file has been personalized for each registered user, do not mix files from differentregistered users. The ‘Nodus Preferences’ file should be placed together with the Nodus 3.2application file (i.e. both on the desktop or both in the same folder) or it can be placed inPreferences folder (System 7) in the System Folder.Nodus 3.2 is available in two versions: the standard version (Nodus 3.2) which runs on anyMac with 68020/30/40 CPU and a FPU and the Quadra version (Nodus 3.2Q) which runs onlyon Macs with a 68040 CPU (it is considerably faster than 3.2). Nodus does not run on Macswithout a floating point unit (FPU) or with a disabled FPU (like the 68LC040). However, ifyou want to test Nodus, the Nodus ftp-site has a software patch ‘SoftFPU’ that can replace theFPU (this is very slow though, so you want to upgrade your Mac if you are going to use Nodusextensively).You are allowed to use either one or both versions of Nodus. Both versions are completely filecompatible with each other and use the same Nodus_Preferences file. Both versions are also32-bit clean and compatible with the 68040 cache on the Quadras (using the cache results infaster performance).

Setting up Nodus II-9

I I I-10 Modeling

III. MODELING WITH NODUS

Introduction 9The Mathematics of Compartmental Modeling 1 1

Linear cable theory .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Compartmental models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Equivalent circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Membrane surface.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Passive Membrane Models 1 3Morphology.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Cable parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Nodus Implementation of Compartmental Models 1 5Reduced models .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Weight factors .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Node and branch connections...................................................... 16Tree format model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17An example of a model reduction.................................................. 18

Excitable Membrane Models 2 0Hodgkin Huxley equation.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Equivalent circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Simulation of Synapses and Connections 2 2Presynaptic transmitter release.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Postsynaptic conductance .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Connections between neurons .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Integration Methods 2 4Hybrid Euler method................................................................ 25Fehlberg method..................................................................... 25Experiments.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

______________________

IntroductionThis chapter describes how Nodus models neurons and small networks. It introduces theunderlying mathematics and has some practical suggestions about making compartmentalmodels. Modeling methods particular to Nodus are defined. Finally it specifies the differentintegration methods that are available.This chapter does not pretends to be a complete cookbook about modeling. Different ap-proaches to modeling are possible. The big categories are single cell versus (small) networksimulations and passive membrane versus excitable membrane models. All these modelsdescribe the electrical status of neurons. There is now a lot of interest in adding (limited)simulations of chemical events to these models, for example the movement and concentration ofcalcium ions. Which type of model is selected depends on the available experimental data andthe particular interests of the modeler.An important issue in creating a model is the specification of the model parameters. An idealmodel would have all its parameters based on hard experimental data. Usually only subsets ofdata are available, so that several parameters need to be estimated. Different model categoriescontrast in which model parameters are emphasized.

Modeling III-11

In passive membrane models detailed morphology is usually the key issue, while the abstractionis made that for the studied phenomena voltage dependent ionic currents are not critical.Excitable membrane models emphasize the ionic currents, with often a very simple morphologyof the neuron (one compartmental models). In most network models connections between theneurons are more important than their morphology or their membrane properties.Nodus is suitable for simulations of all these model categories, but in always a realistic,biological approach is emphasized. All parameters have to be specified by the user, Nodus doesnot implement random variation of parameters. In the near future new modeling options will beadded, including ion concentrations.The consensus in the modeling community about what are the characteristics of a good modelevolves. The aspirant modeler should consult the recent literature, which has become quiteextensive. See the Appendix for a list of good introductory papers and books.

Figure III/1: compartmental models (right) can preserve the detailed morphology of theoriginal neuron (left).

I I I-12 Modeling

The Mathematics of Compartmental ModelingNodus uses compartmental models to simulate the electrophysiology of neurons. The compart-mental approach is easy to use and makes accurate modeling of both anatomy and electro-physiology possible.

Linear cable theoryThe linear cable theory makes exact mathematical formulations of neurophysiological eventspossible, but its practical use for realistic modeling is limited. Linear cable theory describes theelectrophysiological events in a neuron by a single differential equation and for every electricalevent specific solutions of this equation have to be obtained. The complexity of these solutionsis very sensitive to the complexity of the model so that only simple models (e.g. ‘ball and stick’neuron model, 3/2 power branching, etc.) can be handled in practice.Linear cable models are often inadequate to simulate the behavior of real neurons with localinteractions among dendrites, inhomogeneous distribution of synapses and ion channels, etc. Itis also difficult to incorporate non linear behavior (like voltage dependent ionic currents) in linearcable models.

Compartmental modelsCompartmental modeling is derived from linear cable theory. The basic assumptions are that theneuron is a system of connected membrane cylinders in which the intracellular current flow isessentially parallel to the cylinder axis, and that the resistance of the extracellular medium isnegligible. Instead of describing the whole neuron by one large and complex equation, theneuron is divided into many small parts called compartments and the electrophysiology of eachseparate compartment is described by a single equation. This equation is simple because thecompartments are kept small enough to be considered isopotential (i.e. the membrane voltage isconstant over the whole compartment). For passive compartments (i.e. a compartment withoutvoltage dependent ionic currents) the equation is always the same, for excitable compartmentsterms describing the ionic conductances have to be included.The first step in compartmental modeling is to divide the neuron in compartments, for mathe-matical reasons only cylinders and spheres are used. It is evident that accurate modeling ofmorphological details is possible, though it will not always be necessary to make the model ascomplex as in the example in Fig. III/1. All the compartments in the example, except the soma,are cylindrical with a length L and a diameter D:

D

L

Figure III/2: a cylindrical compartment with length L and diameter D and bilateral connections.

Equivalent circuitThe compartment can be reduced to an electronic circuit which controls the membrane potential(Fig. III/3). The equivalent electronic circuit consists of a part describing the current flowingthrough the cell membrane, with a membrane capacitance (CM) and a potential source (E)coupled to a membrane resistance (RM), and a part describing the current flow to othercompartments over a cytoplasmic resistance (RI). The values of CM, RM and RI depend on thesize of the compartment and on neuron specific equivalent cable parameters (Cm, Rm and Ri).

CM = Cm πD LEq. 1

Modeling III-13

RM =Rm

πD L Eq. 2

RI =4 R

iL

πD2

Eq. 3

E

RMCM

RI

Figure III/3: the equivalent circuit for an isopotential compartment.

The potential source E in a passive compartment is the difference between the membranepotential and the resting membrane potential, in an excitable compartment it is made up ofseveral ionic conductances coupled in parallel (see Fig. III/7 and Eq. 28). The change inmembrane potential (∂E) in a compartment #n with a synaptic current Is and an injected current Iecan be described by a differential equation:

∂E n∂t =

E n − Er

RM n CMn+

E n − Ek

RInkk

∑CMn

+I s

CMn+

I e

CMn Eq. 4

The first term in equation 4 describes the current flowing through the cell membrane when themembrane potential is not equal to the resting membrane potential (Er), the second term is thecurrent flow to other compartments (#k) and the last two terms are the synaptic and injectedcurrents. Each compartment in the model can be described by this simple equation: only thevalues for E, RM, CM, RI and k change. Mathematically compartmental modeling is simplebecause the same equation is always repeated. The complexity of the neuron model is containedin the size specific parameters RM, CM and RI, in the number of compartments and theconnections between them. Nodus contains a loop which calculates equation 4 for eachcompartment (the parameters are stored in arrays), this loop is entered at least once during eachintegration step.From the size specific parameters RM, CM and RI two important values can be derived: the timeconstant τm and the space constant λ for the compartment. The time constant τm for the simplecapacitive circuit describing a compartment is given by:

τm = RMiCM

i or τm = R mCm Eq. 5

The space constant λ for a cylinder is the length of a cable with the same diameter D that has RMequal to RI:

I I I-14 Modeling

λ =R m Dn

4Ri Eq. 6

The electrotonic length ln of a compartment is its length relative to the space constant λ:

ln =L n

λ Eq. 7

The electrotonic length ln of a compartment is an important measure in determining the electricalaccuracy of the model, if it is too large the compartment cannot be considered isopotential.Another important electrophysiological parameter for neurons is the input resistance RN (Eq. 8).It is measured by injecting a steady current I in the cell and it depends both on the localmorphology (different RN in the soma compared to the dendrites) and the cable parameters.

RN

= ∆E∆I Eq. 8

Membrane surfaceMembrane surfaces for the spheres and (hollow) cylinders (Eq. 1,2). are computed by standardgeometric equations. For spherical compartments the “holes” in the membrane surface causedby connections to dendritic or axonal cylinders are subtracted from the computed surface. Forcylindrical end compartments (at the tip of a dendrite or axon) the surface of the closed end isadded to the surface of the cylinder.Inaccuracies in measurements of the size of compartments (due to shrinkage or membranefolding, see next section) usually underestimate the membrane surface. In Nodus correctionscan be made for underestimation of membrane surface at 2 levels. For correction of knownmeasurement errors one can Scale Sizes of all compartments (Neuron menu); lengths anddiameters can be scaled separately. The changes in compartment lengths and diameters willaffect the values for RM, CM and RI.An alternative solution, appropriate to compensate for membrane folding, is the use of a globalscaling factor SF which changes for all compartments CM and RM, while leaving RI and τmunchanged:

CM ' = SFCM RM ' = RMSF RI ' = RI Eq. 9

Passive Membrane ModelsPassive membrane models do not contain voltage dependent ionic currents. The neuronalmembrane is considered ‘linear’ (i.e. the input resistance is constant) over the voltage range thatis simulated. They are the most popular type of model, partially because most of the neededparameters are easy to obtain.

MorphologyTheoretically the best passive membrane model would be one that is a detailed morphologicalequivalent of the simulated neuron, with all the cable parameters measured in the same cell. In alot of preparations it is difficult or impossible to get all these data out of one cell, therefore anacceptable approach is to combine accurate morphology of one neuron (obtained from cameralucida measurements on a neuron labeled by intracellular injection of HRP, Lucifer Yellow, etc.)with average physiological data from several neurons.

Modeling III-15

Often some of the cable parameters have to be found by trial and error: several values are usedin the model till experimental measurements can be replicated during simulation. Sometimes oneis confronted with the problem of multiple possible model parameter values; where one cannotdetermine from the available experimental data which of these parameter values is the “real” one.This problem has been reported for a limited number of neuron models only.One can argue that it does not make much sense to use a detailed morphological model if averagephysiological data are used. Usually there is a great variance in morphological details of bran-ching (from second or third order on) between functionally equivalent cells, both in vertebratesand in invertebrates and the branching pattern in some neurons may change over time. Theaccuracy of morphology may be illusory. Because the measurements are done on fixed anddehydrated material, the neuron has shrinked and the amount of shrinkage can be different fordiverse parts of the cell. The shrinkage can be estimated by measuring some easy to recognizestructures before (for example on photographs of a Lucifer Yellow labeled cell) and afterpreparation but the results will be approximate.Computation time is related to the size and complexity of a model and simulations of large,detailed models may take a lot of time on microcomputers. It makes sense to reduce the size ofthe model to increase calculation speed, but there is no golden rule on where the best balancebetween accuracy and computation time is situated. A good approach is to design first a detailedmodel, then reduce it to about 30 to 150 actual calculated compartments (see the next section onhow to do this) and compare the reduced model and the detailed model for importantphysiological parameters as input resistance, time constants and attenuation of membranepotentials. Increase the complexity of the reduced model if necessary, or reduce it further ifpossible.Compartments should be neither too large nor too small. Compartments are supposed to beisopotential, this puts an upper limit on their size. It would seem that very small compartmentsgive a greater accuracy, but this is not true in computer simulations because extremely small (orlarge) numbers give larger round off errors and may even cause “numeric overflow” (i.e. thenumber is too large to be represented in the numeric format used by the computer). Try to keepconnected compartments to a similar size, large differences in size between connected compart-ments may also give larger calculation errors. A good measure for the ‘electrical’ size of acompartment is its electrotonic length (see previous section) which should be between 0.200λand 0.015λ.

Cable parametersThe delicate spot in most passive membrane models are the equivalent cable parameters; in a lotof cases their values cannot be measured experimentally. Most authors take for specificmembrane capacitance the “universal” value of 1 µF/cm2, though one can find measured valuesof 0.3 to 5 µF/cm2. The discrepancies in these measurements are probably due to under-estimation of the membrane surface.Specific membrane resistance varies between different species and between different neurons inan animal. Membrane resistance has to be defined for passive compartments, in excitablecompartments it is the inverse of the sum of all voltage dependent ionic conductances.One should try to find an exact value for Rm. Two approaches can be used: either Rm iscalculated from time constant data (Eq. 5) or Rm is found by trial and error from input resistancemeasurements in the model. No matter which method is used, one should always use the otherone as a final check on the accuracy of the parameter value.To calculate the time constant accurate measurements of the passive response to a hyper-polarizing current step or the relaxation phase after a depolarizing pulse should be made. Thechoice of depolarization versus hyperpolarization depends on the nonlinear behavior of the celland RN: stay within a linear range of membrane potential.

I I I-16 Modeling

The potential response is usually determined by several time constants, these can be found bythe exponential peeling method. Some time constants may depend on activation or inactivationtime constants of ionic currents instead of the shape and cable parameters of the cell. Rm can be

determined from the slowest time constant τm of the neuron (Eq. 5).Another method is to find Rm with simulations. The input resistance at the soma should bemeasured in the original preparation in the linear range of membrane potential. If the electrodeleak was small this value is mainly determined by the size and shape of the cell (which aresupposed to be replicated accurately in the model) and the membrane resistance. RN for theneuron model is measured in simulations with different values for Rm till a good approximationis found. If RN can also be measured at others sites (axon, dendrites) one should do so. Thismethod is superior to using exponential peeling data if Rm is not constant in the cell. Someauthors found that to get an accurate model, Rm had to be much larger in the dendrites than inaxon and soma (reflecting differences in local ionic conductances). Nodus supports variablemembrane resistance in a neuron model.Cytoplasmic resistance can be measured if the axon or a similar structure can be penetrated attwo different sites to measure the attenuation of an injected current pulse between these twopositions. Often the neuron is too small to be sticked at two sites and even if the experiment ispossible the actual measurements may be made inaccurate by bridge imbalances, etc. Mostauthors therefore use values obtained in other neurons of the same animal or of the samephylogenetic class. Simulations should be done for a range of cytoplasmic resistances if nomeasured values are available.Nodus allows free mixing of passive and excitable compartments in a neuron model. Anycompartment can also have postsynaptic currents and/or presynaptic transmitter release.

Nodus Implementation of Compartmental ModelsNodus uses an experiment look-a-like approach to modeling neurons and emphasizes theimportance of morphology. This is implemented by placing the “nodes” of the equivalentcircuits at the center of the compartments (Fig. III/4) and by using alternative methods to lumpbranches together.

1 2

Figure III/4: equivalent circuit for a node connection between 2 cylindrical compartments.

Reduced modelsThe computation time for a simulation depends on several factors: the integration method, preci-sion of the method, size of the model and total number of conductances. One can simplify themodel of a neuron to keep the number of compartments (and the computation time) reasonable.The classic way to simplify dendritic trees is to lump branches together into an equivalentcylinder, i.e. to use one large dendrite instead of several small ones. An extreme example of thismethod is the ‘ball and stick’ model used in linear cable models: one spherical somacompartment and a large cylindrical compartment equivalent to the axon, the dendrites, etc.

Modeling III-17

Alternative methods are used in Nodus to decrease the number of compartments with a constantapparent morphological detail: weight factors and branch connections. These techniques areconceptually simpler and make switching between reduced and complex models easy. Noduscan make reduced models automatically.While these methods can make models more manageable, one should realize that there is alwaysa loss in accuracy. Inexperienced users should not use weight factors or branch connections;one make nice models in Nodus without ever using them. Nodus defaults always to (standard)node connections and a neutral weight factor (of one).

Weight factorsWeight factors are a way to lump branches together without changing their size. Instead oflumping n branches together into one huge branch, an ‘average’ branch is made and connected ntimes to the parent compartment (Eq. 10-13; Fig. III/6); n is the weight factor. All com-partments in the averaged branch are calculated only once by Nodus; there can be no differencein membrane voltage between the n branches. Mathematically this is similar to lumpingbranches together, the advantage is that a “normal", morphological size of the branch is used.

Node and branch connectionsBranch connections are used to increase the electrical accuracy for some connections in themodel, without increasing the number of compartments. In a classic compartmental model allcompartments are connected by node connections: the end of one cylinder is connected to theend of the next cylinder, etc. (Fig III/4) If several compartments connect together at one node(for example where a branch splits), each compartment is connected to all other compartments.

RInode , 1→2

= 2R

in

L1

πD1

2 +L

2

πD2

2

Eq. 10

RInode , 2→1

= n RInode , 1→2 Eq. 11

1 2

Figure III/5: equivalent circuit for a branch connection between 2 compartments.

I I I-18 Modeling

If more than 2 compartments are connected by nodes to the same parent compartment, there iselectrotonically no difference between a binary branch (the parent splits into 2 similar sizedbranches, typical for vertebrate neurons) or a continuation of the parent branch (Nodus assumesthis for the first connection) and a smaller side-branch (invertebrate neurons).If the number of compartments in a parent branch is reduced, the accuracy of the model isdecreased because several side-branches, which do not originate from the same point on theparent branch, will have to be connected to the same node.Branch connections connect to the center of the cylindrical compartment of the parent branchinstead of to the end (Fig. III/5). If several compartments make branch connections to the samecompartment they are not connected to each other. Current flow has to pass through the parentbranch first, which is more accurate if the compartments are equivalent to branches which do notconnect to the same point on the parent branch.

RIbranch , 1→2

= 2R

in

L2

πD1

2

Eq. 12

RIbranch , 2→1

= nRIbranch , 1→2 Eq. 13

Branch connections allow more accurate modeling of complex branching from a lumped parentbranch. For each parent compartment connections can be made at two sites (as a node and as abranch), with a distance of halve the length of the compartment between them. To use branchconnections the tree format model option has to be on.Branch connections are not necessary to use compartmental models; node connections aresufficient. Use branch connections only if needed and if you understand the concept. They areparticularly useful in modeling invertebrate neurons were a lot of thin dendrites originate close toeach other from a thick neurite. They can also be used to model dendritic spines. They are lessuseful in neurons were dendrites show binary branching (as in vertebrate neurons).

Tree format modelThe tree format option for neuron models helps in automatically creating and checking connec-tions between compartments.A tree format neuron model has to be to defined in a centrifugal way. First the soma is defined,then branches originating from the soma, etc. In other words: if each division increases theorder of the more distal sections of the branch, then the low order compartments should bedefined before the high order compartments. The user is free to define either all compartmentsof a main branch first and then the compartments of its side-branches, or to define the com-partments of a complete side-branch before the more distal parts of the main branch are defined.If the tree format model is selected Nodus enforces some simple rules for the neuron model:- compartments can be connected by only one connection.- a spherical compartment cannot be connected to another spherical compartment.- all connections and their weight factors are defined at the “parent compartment” (the com-

partment proximal to the soma, having the lowest order of branching). Connections can bechanged or deleted only in the parent compartment definition window.

- connecting compartment #n to #k automatically connects #k to #n.- if compartment #k and #l are connected to the cylindrical compartment #n by node con-

nections, then #k and #l are also connected to each other by a node connection (“crossconnection”). Node connections to spherical compartments are not interconnected.

- compartments connected as a branch can have only one parent compartment.- reverse weight factors (i.e. child to parent) are always equal to one.

Modeling III-19

An example of a model reductionThe goal of simplifying a model is to reduce the number of compartments to a minimum withrespect for significant morphological details like the number of side-branches, the distance be-tween their point of origin and their cross section and membrane surface. The use of weightfactors and of branching connections to obtain this goal is demonstrated in Fig. III/6.Fig. III/6 shows a part of a neuron model: a branch with 5 side-branches, connected to a largerbranch through compartment C. This model is a simple example: usually the side-branchesthemselves will also consist of several compartments. In the original model all connections arenode connections and 13 compartments are necessary to describe the complete branch.The first step in making a reduced model is to lump the compartments of the parent branchtogether. The choice of which compartments are to be lumped depends on two factors: thediameter of the compartments should be comparable and the distance between the branchesconnected to these compartments should be either small or have no influence on the simulatedelectrophysiological events. In this example the choice is simple because some compartmentshave already a common diameter; 3 compartments can be fused to a long compartment D in and4 small compartments to a compartment E (Fig. III/6B). If the diameters of the compartmentsselected for lumping are not identical, an average diameter has to be calculated (Eq. 14-17).

AA

B

C D E

F

G

B

b b bn

n n

A

B

C D E

G

F

b b

C A

B

C D E

F

G

2 3

Figure III/6: Successive steps in reducing a branch of a compartmental model (A). Firstthe main branch is lumped together and all node connections (n) areconverted into branch connections (b) (B). Then the small branches arelumped together and connected with weight factors (C).

I I I-20 Modeling

The connections between compartments C and D and between D and E remain node connections(n). The connections between the side-branches and the parent branch (F to D and G to E)become branch connections (b). The position on the parent branch is more accurate than itwould be with a node connection at the end of the respective parent compartments, but thedistance in origin between the 2 side-branches F and between the 3 side-branches G has dis-appeared. The effect of this reduction on the electrical accuracy of this model is limited becausethe branches F and G are small and far removed from the soma of the model (interposition ofseveral compartments: D, C, A,…).The next step is to lump the side-branches together into one, average branch and to connect thisbranch n times to the parent branch; the 2 branches F and the 3 branches G are lumped together(Fig. III/6C). The size of the lumped compartment is not just the average of the lengths (L) anddiameters (D) of the original compartments. The important electrical sizes are the membranesurface of the compartment (for CM and RM) and its length divided by cross section (for RI).Calculate first the surface (S) and length divided by cross section (C) of all the (cylindrical)compartments:

S = πLD Eq. 14

C = 4L

πD2

Eq. 15

Calculate the averages of the surfaces (Sa) and cross sections (Ca) and use these to calculatelength (La) and diameter (Da) of the lumped compartment:

La =S a

πD a Eq. 16

D a =4S a

π2C a3

Eq. 17

An important characteristic of branch connections is their asymmetry. From parent compartmentD it looks like there are 2 child compartments F, but from F there is only one D. This gives aweight factor of 2 for D->F and a weight factor of 1 for F->D. For all connections only theweight factor from parent to child has to be supplied, the reverse weight factor is calculated andcontrolled by Nodus.The use of branch connections and weight factors has reduced the number of compartments inthis part of the example from 13 to 5! Of course there is a decrease in morphological accuracy inthe reduced model. In most simulations this will not matter. As long as the branch beginning atcompartment C is completely passive and the membrane voltage is monitored in compartmentsconnected to A or B, the effect of the reduction on for example RN will be small. The reductioncan be acceptable for current injections (as for example in synapses) in all the side-branches Fand/or G together, if membrane potential is monitored in compartments connected to A or B: theeffect of the reduction will be greater, but in most cases not substantial. The reduction is notacceptable if one monitors membrane voltage in one of the reduced compartments or if onewants to study local interactions between side-branches F and G. The example shown here isreproduced in the neuron models ‘Test-cell 5a’, ‘Test-cell 5b’ and ‘Test-cell 5c’ on the NodusMaster disk. ‘Using the examples’ shows numerical results for current injections in thesemodels.It is important to check the accuracy of a reduced model. The best way to do this is to compare itwith the original, complex model, but if this is not available one should compare with a modelthat has at least twice the number of compartments.

Modeling III-21

Compare the reduced and complex model by repeating simulations of the relevant experiments.Significant values are in most cases values that can be compared with biological measurementslike the RN and τm measured in the soma (in some models other structures like a large axon maybe more important) or size and timing of action potentials and synaptic events. The acceptablerelative error will vary, but in most cases errors of up to 5% will be no problem (one compareswith biological events which show even greater variability). Another way to keep reducedmodels to an acceptable level of accuracy, is to start with the complete, complex model and thenreduce it progressively till the error becomes too large.

Excitable Membrane ModelsCompartments can have excitable membrane in Nodus. Any voltage dependent ionicconductance that can be described with Hodgkin-Huxley like equations can be included. Theionic conductances are defined apart from the neuron model definition.

Hodgkin Huxley equationsHodgkin and Huxley based their equations on a model of the conducting channel that consists ofactivation and inactivation gates, each with a voltage dependent probability of being open (M andH). An important simplification in this model is that though several activation and inactivationgates can be connected in series in one channel, all act independently of each other and all obeythe same voltage dependent equations. Transitions between the open and closed form of a gatefollow the simple reaction:

Mclosed β

α M open Eq. 18

Mclosed

= 1 −M open Eq. 19

The rate factors α and β are voltage dependent. The equations for these rate factors are of thegeneral form:

α = d + E

c + e(d + E ) / f

( )b

orα = a + bE

c + e( d +E ) / f

with a = d b Eq. 20

With a, b, c, d and f as terms which are specific for each conductance and E is the membranepotential. Singularities may be found in Hodgkin Huxley like equations when c=-1. If E is theopposite of d then the denominator becomes zero. Nodus obtains a value for α at this singularpoint by interpolation between values around the point, but the rate factor functions may stillbehave irregularly.Opening and closing of the gates can be described with the differential equation:

∂M∂t = α 1 −M( ) − βM

Eq. 21

From this equations follows the steady state open gate fraction M∞:

M ∞ = αα +β Eq. 22

and the gate time constant τM:

τM

= 1α +β Eq. 23

I I I-22 Modeling

The ionic conductance itself can be described by one of the following equations:

G (E ,t ) = g Eq. 24

G (E ,t ) = gMx

Eq. 25

G (E ,t ) = gMxH

zEq. 26

g-bar is the maximum conductance, which may be proportional to the membrane surface of thecompartment (in the Nodus dialog windows and printouts it is marked as Gmax).Equation 24 describes a leak conductance, Eq. 25 and 26 are “true” voltage dependent ionicconductances with respectively x activation gates M, and x activation gates M and z inactivationgates H. Note that in Hodgkin-Huxley like equations inactivation is treated as another“channel”, but as inactivation increases the channel closes (go to zero) and vice versa.

Equivalent circuit

CM

RI

E L

GL

E Na

GNa

E K

GK

Figure III/7: equivalent circuit for a compartment with voltage dependent Na+ and K+

currents and a leak current.

In compartments with excitable membrane the equivalent circuit is expanded, it includes for eachvoltage dependent current a variable conductance and a potential source (Fig. III/7). The circuitshown is for a compartment with the classic Hodgkin and Huxley currents.The ionic current I is determined by the conductance and its reversal potential Ej:

Ik

= Gk

E , t( ) E − Ek( )

Eq. 27

The first term in equation 4 is replaced by a term describing the voltage dependent ionic currents:

∂E n∂t =

GjE n,t( ) E n − E

j( )j∑

CMn+

En − Ek

RInkk

∑CMn

+Is

CMn+

Ie

CMn Eq. 28

For j ionic currents the voltage and time dependent conductance Gj(E,t) (conductance is theinverse of resistance) is calculated and multiplied with the potential source which is determinedby the difference between the membrane potential and the conductance specific reversal potentialEj.

Modeling III-23

To model action potentials and other voltage dependent phenomena accurately, voltage clampdata for all ionic currents present in the neuron should be obtained to construct Hodgkin-Huxleyequations. This represents a lot of work, and may not be experimentally feasible. Therefore alot of authors use published equations. Try to measure the time constant, reversal potential andmaximum conductance of the important ionic currents and adapt the “standard” equations,because these values vary between preparations. If these values cannot be measured directlywith voltage clamps, find indirect measurements (refractory period, period of firing, etc.) andcorrect the values in the simulation by trial and error.Equations derived from voltage clamp experiments may not perform as expected in modelswhere all the ionic currents were included. This is usually a consequence of the artificial condi-tions under which voltage clamp experiments were done (blockers, unusual ionic concentra-tions, etc.); in such cases Gmax may be changed.A small library of equations for voltage dependent ionic conductances is included on the NodusMaster disk. Nodus can plot any factor related to ionic conductances: α, β, M∞, H∞, τM, τH and

G∞ in factor versus membrane potential plots and M, H, τM, τN, G and I dynamically duringsimulations. These plots can help in developing new Hodgkin-Huxley like equations or inadapting existing equations to describe the conductances in the model.

Simulation of Synapses and ConnectionsSeveral functions to simulate synapses are available to the user. One can also implement specialfunctions with user-defined processes (not available in Nodus 3.1).

Presynaptic transmitter releaseIn network models presynaptic activity fires synapses. Presynaptic transmitter release can beconstant or variable. In both cases transmitter is released only when the presynaptic potential ishigher than a threshold potential. With constant transmitter release the release is signaled to thepostsynaptic cell once each time the threshold is crossed, where it arrives at a time t0 which isneeded to compute the postsynaptic conductance (use Eq. 33 or 34). With variable transmitterrelease the amount of transmitter is signaled continuously to the postsynaptic site (as long as thepresynaptic potential is over the threshold); there is no t0 available (use Eq. 32, 35 or 36).Three types of variable transmitter release Ts are available:

Linear to presynaptic potential:

T s = b + f E − Eth( ) , T s ≥ b

Eq. 29

The minimal transmitter release is the base amount b. The variable part is determined by thefactor f and is computed relative to the threshold potential Eth.

Exponential to presynaptic potential:

T s = b + f eE − E

th( ) / c, T s ≥ b

Eq. 30

The minimal transmitter release is the base amount b. The variable part is determined by thefactor f and the characteristic potential c; it is computed relative to the threshold potential Eth.

Process dependent

T s = U E , t ,…( ) , T s ≥ bEq. 31

The transmitter release is determined by a user defined function U which is defined as a separateprocess.

I I I-24 Modeling

Postsynaptic conductanceNodus has five types of postsynaptic conductances Gs, which are grouped in 2 types. The type1 synapses should be used if presynaptic transmitter release is constant or in single neuronmodels; type 2 synapses should be connected to variable transmitter presynaptic sites.Constant conductance (type 2)

G s = gT s Eq. 32

g-bar is the maximum conductance (Gmax), which may be proportional to the membrane surfaceof the compartment. The postsynaptic conductance is completely determined by the presynapticvariable transmitter release Ts.

Alpha function (type 1)

G s = gT s t αe−αt / τ

Eq. 33

The postsynaptic conductance is determined by the alpha function, which is controlled by 2factors: α and τ. The alpha function is computed relative to the “start” of the synaptic firing (t0= 0). Nodus uses a normalized version (i.e. 0<=Gs<=1), it should be used with constant

transmitter release. Factor α determines the steepness of the initial slope of the synaptic conduc-

tance, usually the value α=1 is used. The time to peak τ determines when the alpha functionreaches its maximum conductance.Dual exponential function (type 1)

G s = gT s e−t / τ o − e

−t / τ c( )Eq. 34

The postsynaptic conductance is determined by the dual exponential function, which iscontrolled by two time constants. The dual exponential function is computed relative to the“start” of the synaptic firing (t0 = 0). Nodus uses a normalized version (i.e. 0<=Gs<=1), it

should be used with constant transmitter release. The open time constant το determines how fast

the conductance increases, while the close time constant τc controls its decrease.

Conductance dependent (type 2)

G s = gT sG E ,t( )Eq. 35

The postsynaptic conductance is voltage dependent and controlled by a Hodgkin-Huxleyequation. Conductance dependent synapses should be used with variable transmitter release; thesynapse can only be shut off by decreasing Ts to zero. One can model NMDA-receptors withthis type of postsynaptic conductance.Process dependent (type 2)

G s = gT s U E ,t ,…( )Eq. 36

The postsynaptic conductance is determined by a user defined function U which is defined as aseparate process.

The postsynaptic current Is is given by:

I s =G s E − E s( ) Eq. 37

Es is the synaptic reversal potential.

Modeling III-25

The electronic circuit for a passive compartment with one synaptic conductance is:

E

RMCM

RI

E S

GS

Figure III/8: equivalent circuit for a passive compartment with one synaptic conductance.

Postsynaptic currents can be used in both network and single cell models in Nodus. In networkmodels the presynaptic cell drives the synapse; in single cell models the user has to specify asynaptic firing time.To improve computation speed the user can specify a synaptic switch off time. This controls theswitching off of alpha function or dual exponential function postsynaptic conductances.Without this switch off all synapses that have started firing would go on continuously, evenwhen the actual conductance is infinitesimally small. The synapses will be switched off whenthe simulation time equals t0 plus switch off time. Note that other types of synapses are alwaysconnected to variable presynaptic transmitter releases, they will be switched off when transmitterrelease goes to zero.

Connections between neuronsConnections between neurons in a network are defined in a straightforward manner. A pre-synaptic site is connected to a postsynaptic site and a delay time is specified. The delay time canbe used to simulate axons; the presynaptic transmitter release will arrive at the postsynaptic siteafter the delay time has passed.A single presynaptic site can have connections with multiple postsynaptic sites (simulatingmultiple branchlets at the end of the axon). A single postsynaptic site can also have multiplepresynaptic sites.One has to be careful about connecting the right type of transmitter release with the right synapse(type 1 or 2) . Nodus does not check for this kind of error in connections and the result will besynapses that behave strangely (not firing at all, or firing infinitely). One should connectconstant transmitter release to postsynaptic type 1 synapses (alpha functions or dual exponentialfunctions) and variable transmitter release to type 2 synapses (constant , conductance or processdependent).

Integration MethodsTwo integration methods are available: a fast hybrid Euler method for initial exploratoryexperiments and an accurate, slower fifth order Runge-Kutta method for confirmation of thefinal results, both with variable time steps. All simulation variables and integration steps arecomputed in double precision.

I I I-26 Modeling

Both integration methods are sensitive to the stiffness of the differential equations, they willcompute slowly if membrane voltage or concentration is changing very fast and/or if very smallcompartments are present in the model.The integration methods control the minor time step ∆t. Nodus uses in simulations also a majortime step, which is specified by the user. The major time step is used to control the switchingon or off of experiments. The minor time step is always less than or equal to the major timestep.

Hybrid Euler methodThe hybrid method (Moore, J.W., and Ramon, F.: On numerical integration of the Hodgkin andHuxley equations for a membrane action potential. J. Theor. Biol., 45 (1974) 249-273) is a fastforward Euler method. (Eq. 38). The accuracy of the computation is controlled by settingabsolute maxima for changes in membrane potential and concentration in any compartment. Theminor time step is adapted dynamically to keep the changes below the maxima.

E = E0

+ ∆t∂E

0∂t Eq. 38

For excitable membrane models the hybrid method simplifies the differential equations describ-ing an excitable compartment (Eq. 20, 25 and 28) by making the rate factors for (in)activation (αand β) voltage independent for each time step ∆t. The time constant τM (Eq. 23) and the steady

state open gate fraction M∞ (Eq. 22) are determined from α and β. The approximate solution forM becomes then a simple exponential function:

M = M ∞ + M0

−M ∞( )e−∆t / τ

M

Eq. 39

To increase computation speed a table of the (in)activation factors M∞ and N∞ is calculatedbefore the simulation starts. An information dialog window shows the progress of this compu-tation. The maximum error shown in the dialog window is estimated by halve the differencebetween the calculated value and the value obtained from interpolation between the precedingand the next entry in the table. If this error becomes too large one cannot trust the results fromthe hybrid Euler method. During the simulation the actual (in)activation factors are determinedby linear interpolation between the values in the table.The hybrid method may oscillate when an equilibrium is reached in the simulation (for exampleafter a current injection), more so in small compartments than in large ones. The error of thehybrid method relative to the Fehlberg method is usually smaller than 1%, input resistances andperiod of firing are less accurate than for example maximum amplitude of an action potential.

Fehlberg methodThe Fehlberg method (Forsythe, G.E., Malcolm, M.A., and Moler, C.B.: Computer methodsfor mathematical computations. Prentice-Hall, Englewood Cliffs (1977) pp. 110-147) is basedon the Runge-Kutta integration method. In its standard formulation the Runge-Kutta method isa fourth-order integration method:

E = E0

+k

1+ 2k

2+ 2k

3+k

46 Eq. 40

k1

= ∆t∂E

0∂t

0

Modeling III-27

k2

= ∆t∂ E

0+

k1

2

∂ t0

+ ∆t2( )

k3

= ∆t∂ E

0+

k2

2

∂ t0

+ ∆t2( )

k4

= ∆t∂ E

0+k

3( )∂ t

0+∆t( )

The Fehlberg method requires six function evaluations for each parameter. Five of thesefunction values are combined to produce a fifth-order Runge-Kutta method, four values are usedto produce a fourth-order method. Comparison of these two values yields a relative errorestimate which is used to control the minor time step size.Parameters that are integrated include: membrane potential, concentrations, variable transmitterrelease, synaptic conductance and (in)activation factors for voltage dependent ionicconductances. The rate constants are integrated independent from the membrane potential(sequential mode of integration, Moore (1974)).The Fehlberg method is very accurate (minimum relative error can go down to 10-12), but it canbe quite slow. It is sensitive to the stiffness of the differential equation, i.e. during sudden largechanges in membrane potential (action potential, begin of a current injection, etc.) the minor timestep can decrease enormously. The method may fail if the minor time step gets smaller than theminimum value which can be accurately represented as a double precision floating point number(0.11 10-15). In this case Nodus may temporarily increase the allowed relative error. Anappropriate integration error message is shown; the change in relative error can be monitoredwith the Status Window command.

Experiments

Most experiments settings can change only when major time steps are reached. Some exper-imental parameters (like sinusoidal current injections) are however recomputed at every minortime step.Voltage clamp experiments are simulated by a post-integration correction. A normal integrationstep is performed in either of the integration methods. Then the clamping current is calculatedand the change in membrane potential in the voltage clamped compartments is set to thedifference with the clamping potential.

IV -28 Nodus Reference

IV. NODUS REFERENCE

Nodus Menus 2 7The menu bar .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Popup menus .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Nodus Files 2 8Overview ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Links between files.................................................................. 30Subdefinitions in neuron definition file. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Making backups of Nodus files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35File compatibility with Nodus 2 .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Making New Models 3 6Collect and organize the experimental data .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Entering data into the definition files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Store the original model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Making New Simulations 4 1What is a simulation database.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Simulation database status and available menus .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Selecting the model.................................................................. 43The initial value problem ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Improving simulation speed........................................................ 46

Selecting Simulation Parameters 4 7The selection of a simulation parameter........................................... 48The compartment preselection popup menu.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Error messages about parameter selection.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

______________________

Nodus Menus

The menu barExcept for the File and Edit menus, all other menus in Nodus 3 are file specific (Simulation,Network, Neuron, Conductance). The file specific menu commands are enabled onlywhen a window of the corresponding file type is active (i.e. is in the front) and the commandswill operate on that file when invoked.

Figure IV/1: the Nodus 3 menu bar.

The File menu has a lot of submenus (marked with an arrow pointing to the right) to select onwhich type of file the command should work. To get the submenus press on the main menucommand and then move to the submenu list while keeping the mouse button pressed, releasethe button over the required submenu command.

Figure IV/2: the File menu with the Open submenus.

Nodus Reference IV-29

Several File menu commands can be modified by pressing the shift key and/or option key (seeAppendix for a complete list). The names of the menu commands change as appropriate. Startpressing the shift and/or option key before selecting the menu!Any of the four simulation database windows (Plot Window, Time Window, MeasureWindow , Status Window) activates the Simulation menu. Some simulation menucommands may be enabled, but italicized (Fig. IV/18). This means that one can use thecommand to view settings, but that one cannot change the settings.At the bottom of the Network, Neuron and Conductance menus are lists of names of all thecorresponding files loaded into memory (Fig. IV/3). Some of these files may have windowsassociated with them, they are shown with a check mark. Selecting one of the file names fromthese lists makes it the active window, by either bringing an existing window to the front or bymaking a new window.

Neuron definition files in memory

Figure IV/3: the Neuron menu. At the bottom of the menu is a list of 3 neuron definitionfiles in memory. The Test-cell 7 definition window is active.

Popup menusNodus 3 makes extensive use of popup menus (Fig. IV/4): in most definition windows anddialog windows popup menus are present (see for example Fig. IV/9). Popup menus allow theuser to make fast selections without having to remember names or index numbers.

Figure IV/4: a popup menu shows the selected item (in this example the name of aconductance definition) when it is not pressed (left). When the user pressesthe menu a list of all available menu items is shown (right).

Popup menus require that all definitions and subdefinitions in Nodus are named; naming neuroncompartments can also help in later popup selections. Try to use self-explanatory names, itmakes no sense to have a popup menu full of incomprehensible acronyms or numbers. Do notuse very long names; they may not fit in the dialog windows so that only the first part of thename will be shown.

Nodus FilesNodus 3 can make seven different types of files and some files may be linked to other files. Thecomplete user interface is based upon the Nodus file structure, so it is quite important tounderstand what each file type represents and how the links between files work.


OverviewTwo file types used by Nodus 3 cannot be accessed by the user.

Nodus Preferences file:The ‘Nodus Preferences’ file contains the default and user specified settings for theNodus application. Nodus needs the ‘Nodus Preferences’ file to run, it is personalizedfor each registered user. It cannot make a default preference file in case of disk failure,so be sure to always have a backup of this file available. The‘Nodus Preferences’ file

should be placed together with the Nodus application file (i.e. both on the desktop or both in thesame folder) or it can be placed in the System Folder.Nodus Resume file:

The ‘Nodus Resume’ file is created when the user quits Nodus while a simulation isrunning and the Automatic Saving command is selected. It contains the data neces-sary to continue the simulation after restarting Nodus. Nodus creates and deletes this filewhen appropriate, it is always put at the same desktop level as the Nodus application.

Do not rename this file. If it is renamed or deleted Nodus will not be able to continue the simu-lation after restarting.

Four file types are accessible to the user; the File menu commands New, Open, Close andSave operate on these files. They contain simulation databases and model definitions.Simulation data file:

A simulation data file contains all the data necessary to run a simulation. This includesthe compiled simulation database, initial values, settings for the integration method, forgraphic and text output and for the experiments, and the links to the original modeldefinition files. It does not contain any simulation results.

Network definition file:A network definition file contains the description of a small network model. Thisincludes the local names of all the neurons, links to the original neuron definition filesand data about the synaptic connections between the neurons.

Neuron definition file:A neuron definition file contains the complete description of a compartmental neuronmodel. This includes the cable parameters and morphology of all the compartments andthe topography of the connections between compartments. The file also contains thesubdefinitions for all the ionic currents, synaptic currents or transmitter release sites used

in the neuron model. Ionic current subdefinitions and some synaptic current subdefinitions havelinks to the equations in conductance definition files.Conductance definition file:

A conductance definition file contains the equations in Hodgkin-Huxley like format for aconductance.

Nodus can make one type of output file. It can only be opened from other applications.Text output file:

A text output file contains the results of a simulation in ASCI format. Text is separatedby spaces or TABs, lines are separated by carriage returns. Text output files can beopened as a standard Macintosh text file by any word processing software package orthey can be imported into a spreadsheet, graphics or statistical packages for display or

further analysis of the data.


Links between filesThe four user accessible files are hierarchically ordered by the links possible between them (Fig.IV/5). The advantage of this structure is that information can be spread over different files.Common data has to be defined only once in a file at a lower level of the hierarchy; it can then beused by different models higher up in the file hierarchy.

Files Max number in memory

Simulation databaseIntegration settingsPlot and text output settingsExperiment controls

Simulation Data File

Equations

Conductance Definit. File

Local neuron namesConnections

Network Definition File

CompartmentsIonic Current subdefinitionsSynaptic Current subdefinitionsTransmitter Release subdefinitions

Neuron Definition File

1

1

Links

OR

20

20

Figure IV/5: the Nodus file hierarchy and the links possible between files.

Links between files are always stored and defined in the files at the top of the hierarchy (Fig.IV/6). Links work best if all linked files are in the same folder. The user makes a link byselecting the name of the lower level file with a popup menu in a dialog window belonging to thehigher level file (Fig. IV/4). Links are just like any other piece of data; changes to file links arepermanent only after they have been Saved to the disk file.Nodus finds linked files by their file name, so the user should never change file names (at theFinder level) because then the file will no longer be recognized. All definition files also have ahidden file ID-number which must match the one stored as a link in the file at a higher level inthe hierarchy. The hidden ID-numbers are unique for each file so that all links are alwaysunique. The hidden ID-number changes when a Save As command is done (Figs. IV/6 & 7).At that time the user can change all links in higher level files to point to the new file (with newname and ID-number) by selecting the Update all links in memory check box in the dialogwindow (Fig. IV/6, bottom right); otherwise the files at higher levels in the hierarchy willcontinue to point at the original file (Fig. IV/6, bottom center). Note that at present only links infiles loaded into memory can be updated, disk files are not changed.


On disk In memory Neuron A

ID #9453220 compartments…Link to 'Funny', #4589Subdefinitions, links…

FunnyID #4589am = 0.01 / exp(…)…

Neuron B ID #3738568 compartments…Link to 'Funny', #4589Subdefinitions, links…

Open Neuron 'A' Neuron A ID #9453220 compartments…a link to #4589Subdefinitions, other links…

FunnyID #4589am = 0.01 / exp((40+E)/80)…

User edits 'Funny'

Neuron A ID #9453220 compartments…a link to #4589Subdefinitions, other links…

FunnyID #4589am = 0.05 / exp((40+E)/80)…

Save 'Funny'

On diskSave As 'Fast Funny'without Update all links

Save As 'Fast Funny'with Update all links

Neuron A ID #9453220 compartments…Link to 'Funny', #4589Subdefinitions, links…

FunnyID #4589am = 0.05/ exp(…)…


Neuron A ID #9453220 compartments…Link to 'Funny', #4589Subdefinitions, links…

FunnyID #4589am = 0.01 / exp(…)…


Fast FunnyID #235498am = 0.05 / exp(…)…

Neuron A ID #9453220 compartments…Link to 'Fast Funny', #53849

FunnyID #4589am = 0.01 / exp(…)…


Fast FunnyID #53849am = 0.05 / exp(…)…

Figure IV/6: links between files before and after a Save or Save As command. The ID#numbers are invisible to the user. Refer to the text for an explanation.


Figure IV/7: the Save As file dialog window.

At the bottom of the hierarchy are conductance definition files which contain the equations forionic conductances. They do not have links, but are a good example of the usefulness of thelinked files concept. Several neuron models may use the same set of ionic conducance equa-tions, for example the standard Hodgkin-Huxley equations for the fast sodium and delayed recti-fier currents. These equations have to be defined only once in 2 conductance definition files (thefiles ‘HH Fast Na Current’ and ‘HH Delayed Rectifier’ on the Nodus Master Disk), which canthen be linked to models of the ‘Squid Giant Axon’ and to any number of other models.Neuron definition files can have links to conductance definition files in subdefinitions. Ioniccurrents are defined by specifying a reversal potential and a maximum conductance, which aresaved in the neuron definition file, and a link to a set of conductance equations in a conductancedefinition file. Synaptic currents may also use conductance equations from a conductance defi-nition file. A neuron definition file can be viewed and edited while a linked conductance defi-nition file is not in memory, except for the affected ionic and synaptic current subdefinitions.Network definition files always have links to neuron definition files. A network can consist ofseveral instances of one neuron model (like in the ‘Test 7 network’ on the Nodus Master Disk),or can combine different neuron models. A network definition file cannot be used if the linkedneuron definition files are not in memory.Simulation data files have links to all model definition files which were used as sources tocompile the simulation database. A simulation data file knows only the source model definitionfile selected in the New Simulation dialog by name; for the lower level files (for exampleconductance definition files) only hidden file ID-numbers are available.When a file is Opened all lower level linked files in the same folder are also loaded intomemory, including files connected by “secondary links”. For example when the ‘Test 7 net-work’ network definition file is Opened the linked neuron definition file ‘Test-cell 7’ is loadedand 3 conductance definition files which are linked to ‘Test-cell 7’ are also loaded. No windowsare created for automatically loaded linked files, but their names are listed at the bottom of thecorresponding menus. Select the name in the menu to make a file window.

Figure IV/8: the Open file dialog window.


Note again that successful loading of linked files depends on their file name, which should nothave changed. When a file cannot be found, an extra Open dialog window prompts the user toopen it manually. When the top level file is then Saved, the new file name for the linked filewill be stored. Automatic loading of linked files can be prevented by switching off the Openall linked files check box in the Open dialog window.Changing data in lower level files will affect all linked model definition files higher up. This canbe dangerous if the lower level file is linked to a lot of different model definition files. Forexample if the original conductance definition files of the standard Hodgkin-Huxley equationsare changed to slow the rate factors down for an invertebrate neuron model; not only the inverte-brate neuron model will be affected, but also the ‘Squid Giant Axon’ model! The solution is togive the changed conductance definition files a new name using the Save As command with theUpdate all links in memory check box switched on (while the invertebrate neuron model isin memory and the ‘Squid Giant Axon’ model is not). The ‘Squid Giant Axon’ and theinvertebrate neuron model (do not forget to Save it!) will then be linked to different conductancedefinition files. See Fig. IV/6 for additional information.Simulation data files are not affected by changes in their model files, though some changes mayinterfere with selecting parameters with popup menus (see the section on ‘Selecting SimulationParameters’). Changes in model definition files affect the simulation data file when a NewSimulation database is compiled.

Subdefinitions in neuron definition filesNeuron definition files can contain subdefinitions for Ionic Currents, Synaptic Currentsor Transmitter Release sites. The use of subdefinitions makes the creation and managementof these features very flexible.The subdefinitions contain all the data necessary to implement the feature (see further) except forthe location within the compartmental neuron model. At individual compartments the user canthen select one or several of the subdefinitions to “tie” them to the compartment (Fig IV/9). Ifno subdefinitions are selected at a compartment it is a passive compartment without pre- orpostsynaptic contacts. If a set of ionic currents is selected it becomes an excitable compartment,if synaptic currents are selected it is a postsynaptic compartment or if a transmitter release site isselected a presynaptic compartment. Several subdefinitions can be combined, e.g. to make anexcitable postsynaptic compartment (Fig IV/9), and any combination of different types ofcompartments is allowed within a neuron model. The ‘Squid Giant Axon’ model contains onlyexcitable compartments, all using the same ‘Hodgkin-Huxley Currents’ subdefinition; the ‘Test-cell 6’ model is completely passive, but has different postsynaptic currents in all itscompartments; the ‘Test-cell 7’ model has passive, excitable and postsynaptic compartments.

Figure IV/9: ionic current subdefinition selection in a compartment definition window.This compartment also has one postsynaptic site, but no presynaptic site.


Subdefinitions can be created, viewed and edited separately from the neuron model(s) that usethem by selecting the corresponding menu commands in the Neuron menu. As an example theSynaptic Currents subdefinition dialog is shown (Fig. IV/10), the other subdefinition dialogwindows are very similar.At the left side of the dialog window are the controls and data specific for a selected subdefi-nition. At the top left of the subdefinition dialog window is a subdefinition selection popupmenu labeled as Synapse subdefinition, it contains a list of the names of all the synapticcurrent subdefinitions in memory (from all the neuron definition files in memory). If the neurondefinition file in the front window uses synaptic current subdefinitions, the name of the first oneused will be shown (as in Fig. IV/10: Slow EPSP); if no synaptic current subdefinitions are inmemory None will be shown.Under the subdefinition selection popup menu the data box shows parameters specific for thesubdefinition; in this case a peak conductance, a reversal potential and a variable synaptic con-ductance. If no subdefinition is selected, the data box is empty and all its options are dimmed.

Figure IV/10: the Synaptic Currents subdefinition dialog window.

At the right side of the dialog window are general subdefinition controls and info. At the top is alist of all neuron definitions in memory using the selected subdefinition. Below it are fourbuttons that perform subdefinition management: New, Delete, Duplicate and Rename.Their function is implicit in their names. The Delete, Duplicate and Rename buttons act onthe presently shown subdefinition; the New button creates a new, empty subdefinition with thename Untitled.At the bottom right side are the dialog window controls, the OK and Cancel buttons. Bothclose the dialog window. Pressing OK keeps any changes made to the data box entries, press-ing Cancel discards all changes made to the currently selected subdefinition. The subdefinitionselection popup menu makes a rapid selection of a subdefinition for viewing or editing possible.Note however that changing the selected subdefinition is equivalent to pressing OK withoutclosing the dialog window, any changes to the subdefinition shown will be preserved.An Ionic Currents subdefinition is a set of up to ten ionic currents. For each current itcontains the maximum conductance (either as an absolute value in nS or as a compartment mem-brane surface dependent value in mS/cm2), the reversal potential and a link to the voltagedependent conductance equations in a conductance definition file (except for the Leak, whichhas a constant conductance).A Synaptic Currents subdefinition specifies one postsynaptic site. Up to three differentsubdefinitions can be used in a single compartment. Though Nodus does not prevent therepeated use of the same subdefinition at a single compartment, this is not useful (in networkmodels several presynaptic cells can be connected to the same postsynaptic site, i.e. to the samepostsynaptic subdefinition in a compartment). The subdefinition contains the synaptic peakconductance, the reversal potential and variable conductance data.


The synaptic conductance can be constant; this is useful for synapses with graded transmitterrelease where the user wants to specify the change in synaptic conductance at the presynapticsite. Variable synaptic conductances can be an alpha or dual exponential function (see chapterIII), a voltage dependent conductance defined by a link to a conductance definition file or an userdefined process.A Transmitter Release subdefinition specifies one presynaptic site. A compartment cancontain only one presynaptic site. The subdefinition contains the minimal amount of transmitterreleased and a threshold membrane potential above which transmitter is released. Variable(graded) transmitter release is controlled by linear or exponential voltage dependent functions orby a user defined process.Subdefinitions are stored in the neuron definition files. When a neuron model is Saved allsubdefinitions used by that model are stored in the neuron definition file. When a neurondefinition file is Opened all subdefinitions present in the file are loaded into memory and addedto the lists shown in the respective selection popup menus. Of each type of subdefinition upto 20 different ones can be stored in memory. If a subdefinition with the same name is alreadyin memory, Nodus will add “#0” to the name (or a consecutive number if the subdefinition namealready contained a “#number”) unless Multiple use of subdefinitions is allowed(Preferences command, see next paragraph).Subdefinitions are very flexible. Global changes which affect several or all compartments areeasy to make, while the user also has full control over the features used in any single compart-ment. There is one potentially dangerous feature: the use of the same subdefinition in several(different) neuron definition files. If any changes are made to such a subdefinition, it will onlybe saved (when the user Saves the file) to those neuron definition files which were in memoryat that time; the other files will still contain the old version of the subdefinition! This is usuallyundesirable and therefore subdefinitions can be used in different neuron definition files onlywhen the Multiple use of subdefinitions check box in the Preferences dialog is switchedon (the default setting is off). Multiple use of subdefinitions can be useful in network models,where for example a type of postsynaptic site might be common to different neuron models. Ifthe Multiple use of subdefinitions is enabled, then always Open the Network (thisguarantees that all the required neuron definition files are in memory) instead of opening indi-vidual neuron definition files.

Making backups of Nodus filesOne should always make backups of important files to protect oneself against hard disk failuresor viral infections. This rule applies also to Nodus files, but one has to take into account thespecific properties of the Nodus file hierarchy.- Make your backup copies always at the Finder™ level. Do not use Duplicate because that

Finder command changes the file names. Drag the file icons to the backup disk, the Finderwill copy the files and keep the original files. If you want to store backups in another folderon the same disk as the original files, press the option key while dragging the file icons to thefolder (they will be copied instead of moved).

- Never use the Save As command to make backup files. This command changes the hiddenID-number of the file so that it will no longer be recognized by linked files!

- Always make backups of all linked Nodus files together. They need each other to workproperly. If you need to restore damaged files from backups, it is usually best to restore alllinked files together also.

File compatibility with Nodus 2Nodus 2.0-2.3 neuron and conductance definition files can be read by Nodus 3, but they are notlinked together till they are saved as Nodus 3 files. You should open the Nodus 2 conductancedefinition files before the neuron definition files.


Synaptic conductance is computed by different equations in Nodus 3; synaptic conductancesettings of Nodus 2 files are converted to approximate the old conductances in Nodus 3 but theresults will not be identical. The subdefinition concept was not available in Nodus 2, were thesesettings had to be repeated for each compartment. Nodus 3 tries to collect all ionic current orsynaptic current settings from one neuron definition into one or a few subdefinitions, butsuccess is not guaranteed.If you have a large set of Nodus 2 neuron and conductance definition files you should convertall of them to Nodus 3 format at once. Do not mix the old Nodus 2 and converted or newNodus 3 files in the same folder; it might confuse Nodus if they have the same names.Nodus 2.0-2.3 simulation data files and simulation run files cannot be read by Nodus 3. Theinternal format of the simulation databases has completely changed and simulation plot, outputand experiment settings are managed by new, more user friendly methods.All simulation database computations in Nodus 3 are done in double precision. Because thecompartment capacitance and the linking resistances are computed in double precision highaccuracy simulations of passive neuron models in Nodus 3 may produce slightly differentresults than in Nodus 2 (less than 0.1% for RN and τm in my experience). Nodus 3 is alwaysmore accurate (in Nodus 2 compartment capacitance and the linking resistances were computedin single precision and then converted to double precision if double precision integration wasrequested).

Making New ModelsTheoretical aspects of modeling with Nodus are covered in chapter III. This section offerspractical suggestions about constructing and managing your own models. The systematic ap-proach to modeling presented here applies to doing “real work” with Nodus. To get acquaintedwith Nodus “play around” with the example files and try to make small models.

Collect and organize the experimental dataOne can model to test and explore theoretical hypotheses, or one can model an experimentalpreparation to simulate experiments and test whether scientific knowledge about it is complete.In both cases the modeler has to supply several parameters to Nodus, which usually are derivedfrom experimental measurements.Decide on what type of model is going to be used. Will it have passive or excitable membrane?In a passive membrane model, will Rm be constant or variable? Can the synaptic events beexamined with a single neuron model or is a simulation of the presynaptic neuron(s) with a smallnetwork model necessary? Once such decisions have been made, make a list of all the (sub)-definitions that are going to be used: the number of ionic currents, the number of neurons,different types of synapses, etc. For the (sub)definitions the following parameters are needed:Ionic currents: (approximate) equations in Hodgkin-Huxley format. Reversal potential,

maximum conductance (may be distinct for different neurons or compartments).Postsynaptic sites: (approximate) equations for the variable synaptic conductance. Reversal

potential, maximum conductance (may be distinct for different neurons or compartments).Presynaptic site: constant or graded (variable) transmitter release. Threshold potential for

transmitter release and minimum amount released (may be distinct for different neurons orcompartments). For graded transmitter release the equations describing the amount released.

Neurons: morphological data (number of compartments, sizes of compartments, connections).The cable parameters: Cm, Rm, Ri. Resting membrane potential. Distribution and location ofionic currents, pre- and postsynaptic sites.

Networks: number and types of neurons. Connections between neurons: presynaptic to post-synaptic site types, delay times (to simulate axon length).


Start with collecting all the parameters listed and try to put them in a format that approaches theway the information is structured in Nodus. Usually some parameters are missing. Maybesomething can be found in old lab books or in the literature. If no experimental data areavailable one has to use default values, usually based on experimental data from other prepa-rations. A good example is the specific capacitance; the default value of 1 µF/cm2 is almostnever checked experimentally. When in doubt, consult the modeling literature to see howcolleagues solved similar problems. Often an important aspect of modeling is to examine howdifferent values for an unknown parameter influence the ability of the model to simulate certainexperiments. Nodus makes it easy to change model parameters, so it is not a tragedy if some ofthe initial parameters are found to be unsuitable during actual simulations.While a lot of experimental data may need extensive work to get it into formats compatible withNodus (for example getting conductance equations from voltage clamp data), morphological datacan often be used without any changes. The user can optimize the neuron model in Nodus eithermanually (Fuse Compartments, Split Compartment) or automatically (OptimizeModel) to get acceptable performance during simulations.When the neuron models are large, i.e. they have a lot of compartments, one should examinewhether they can be imported into Nodus. Detailed morphological data are usually stored incomputer files. Transferring these files from other computer systems to the Macintosh is easy,use the file exchange software supplied by Apple. Compare the format of the morphologicaldata with the formats available in the Import Neuron command (see Appendix). If thedifferences are small, try to convert the files yourself. A spreadsheet is usually the best platformfor editing morphological data files, most packages can read text files. Do not forget to save thefile in text format (a golden tip: Microsoft Excel™ does not put a carriage return after the lastrow of a spreadsheet; make a fake row at the end of the spreadsheet by putting a non-numericcharacter in its first cell).If the format of the (large) morphological data file is quite different from the ones available inNodus you may consult the author and request the addition of a new import format. In the meantime organize the data so that it is easy to type them into Nodus manually. Give eachcompartment a compartment number and list its diameter, length, 3-dimensional coordinates (ifavailable) and all connections to other compartments. The soma should be the first compartment(#1), number other compartments from proximal to distal in either of two ways. One can firstnumber the large processes (number the compartments of the stem of the first dendrite from 2 ton, the stem of the second dendrite from n+1 onward, etc) and then number the smaller dendriticbranches. Or all compartments (stem and all branches) of one dendrite can be numbered firstand then the next dendrite (or the axon) can be numbered, etc. The first numbering scheme canhave advantages when variable Rm is used, the second one is more intuitive and corresponds tothe format of most morphology files.

Entering data into the definition filesLinks to files lower in the hierarchy can only be established after these files have been created,therefore one should start by making the files at the bottom of the file hierarchy.All definition file windows have a text box that initially contains the word Comment (Fig.IV/11). Use this space to identify the definition by either quoting references to the original dataor by a note specifying what makes this particular definition file different from similar ones.Conductance definition files:First make the conductance definition files with New Conductance and type the equationparameters into the conductance definition window (Fig. IV/11). Push M to define the acti-vation factor equations or H to define the inactivation factor equations. Save the files withrecognizable names.


If the conductance equations used are approximate, they may need to be changed later. Considerthen a way to mark in the file name the progression of changes to the equations, usually anumbering scheme is appropriate.

Figure IV/11: the empty conductance definition window.

Check whether the equations behave as predicted. Plot (In)Activation factors and TimeConstants over the relevant membrane potential range. This is a quick way to find typingerrors. If the equations were never tested in simulations it might be worth checking them in asmall test model with only one compartment (this will be much quicker). See whether theyreplicate voltage clamp experiments.Neuron definition files :Start with the subdefinitions first. Define all Ionic Currents, Synaptic Currents andTransmitter Release subdefinitions needed in the model. Again, consider that usually a lotof changes need to be made to subdefinitions during the tuning of the model (particularly themaximum/peak conductances are often changed a lot).Decide which compartment labels are going to be used for the selection of simulation parameters(refer to the end of this chapter). One can either name all compartments or only the interestingones (like the soma, dendritic roots, synaptic sites, etc). Another labeling method is to use theStructure type popup menu (Fig. IV/13) to describe the structure of each compartment. Boththe Names and Structure type of a compartment are optional. They are worth using in largeneuron models, because compartment numbers are not very intuitive and may change after anOptimize Model command. Good planning and consistency of compartment labels can makeusing Nodus more rewarding.

Figure IV/12: the default neuron definition window.

Make the compartmental model of the neuron. If the compartmental model can be imported doImport Neuron (and life is easy) otherwise do New Neuron. Type in the Cableparameters for the neuron (Fig. IV/12).


Decide on whether the Tree format and 3-dim coordinates options are going to be used.The rules imposed by the Tree format option are discussed in chapter III. Always use theTree format option, unless it interferes in some way with the neuron model itself. Using 3-dim coordinates has no advantages in Nodus 3.1, it reduces the maximum number ofcompartments from 4000 to 3000. Future Nodus versions may have 3-dim drawings, whichwill need these coordinates.Save the neuron definition file with a recognizable name.If the neuron was made with the New Neuron command change the Number of com-partments into the correct number. Then do Next Compartment to start defining com-partment sizes and connections (Fig. IV/13). Most of this work can be done from the keyboardby tabbing from one text entry box to the next and pressing command-+ to go to the nextcompartment. Note that Nodus automatically connects distal compartments to their proximalparents (if the Tree format option is switched on); these back connections are disabled andcannot be edited. Do not use branch connections or weight factors (explained in chapter III)unless you understand their (dis)advantages. Nodus checks whether all the entered values areacceptable before allowing the user to go to another compartment. Save the neuron definitionfile frequently. Print the complete neuron definition file and check for typing errors incompartment sizes or connections.

Figure IV/13: an empty compartment definition window.

Compartment Names and Structure types are also entered in the compartment definitionwindow (Fig. IV/13). If New Neuron was used this can be done during the entry of compart-ment sizes and connections; if Import Neuron was used this can be done with consecutiveNext Compartments to label each compartment or Go to Compartment # to label onlyinteresting ones. At the same time subdefinitions can be “tied” to the compartment. Save theneuron definition file frequently.Check whether the neuron model behaves as expected in simulations of experiments. Performtuning of the cable parameters, the maximum/peak conductances, etc as needed. If the simu-lations go too slow (with large models) one might make special versions of the neuron definitionfiles for different experiments. One trick is too eliminate all subdefinitions that are not used in aspecific simulation. For example to test input resistance and peel exponentials of a large passivemembrane model one does not need to have the synaptic current subdefinitions. If a lot ofpostsynaptic sites are defined on the model eliminating all these subdefinitions with the Deletebutton in the Synaptic Currents dialog window will create a model specific for the peelingexperiment that computes much faster. One might also consider simplifying the morphologicalcomplexity of the model with the Optimize Model command to reduce the number ofcompartments during the tuning of ionic currents subdefinitions.


Network definition file:Finally the network definition file is made. Be sure to have all the needed neuron definition filesin memory. Do New Network and enter all the network neurons (Fig. IV/14). Press theNeuron definition popup menus in the middle column to add a neuron model to the networkand give it an appropriate Local name. Local names will usually be either specific names (like“Pyloric dilator”) or reflect anatomical location (left, right, dorsal, ventral, etc.); they will beused in the selection popup menus. Press the Next button to enter more than 8 neuron models.

Figure IV/14: the empty network definition window.

Save the network definition file with a recognizable name.Specify the connections between the neurons with the Set Connections command, thenetwork definition window will show the total number of pre- (Out) en postsynaptic (In)connections. Save the file again.

Store the original modelsA final simulation model ready for publication will usually be quite different from the initialmodel entered into Nodus. The initial model however contained unmodified experimental dataand is as such a good condensation of the available biological data. It is worth keeping it as asummary for later reference. The same goes for intermediary models created during the tuningprocess and the progressive series of simulations. Instead of printing all the models out andpasting long listings in the lab book, one can just refer to the model file names in the lab bookand note down simulation results while storing the Nodus files. Again, clear names (with anumbering scheme) and sensible use of the Comment space will help to identify them later on.Remember to store the linked lower level files also.Storing original neuron definition files is important if the commands Fuse Compartments,Split Compartment or Optimize Model are used. These commands are very useful duringthe tuning process, because changes to the cable parameters may alter electrotonic lengthsenough to make compartments too long or too short. But one should not repeat these commandstoo often on the same file, because the neuron model morphology will slowly divert from theoriginal measured data. It is better to go back to a neuron definition file with the originalmorphology, enter all the changes to the cable parameters and Optimize Model again.


Making New Simulations

What is a simulation databaseModel definition files are a good framework to define and manage neuron or network modelsbecause they were structured to look like standard physiological and morphological data. Theyhave however an inefficient format to run simulations with. Nodus compiles all data from aselected model definition file (and from the linked files) into one simulation database. The simu-lation database structure was optimized for simulation speed; unfortunately this resulted in aincomprehensible internal format. The format of the database is invisible to the user and of noconcern as all relevant simulation parameters can be accessed with selection popup menus (seethe next section).There can be only one simulation database in memory. The simulation databases are numberedconsecutively. The simulation number is shown in the simulation plot window title as‘.Sim0001’. The simulation number counter can be reset to zero in the Preferences dialogwindow.The simulation database is a separate entity. The model parameters in a simulation database donot change when the original model definition files are changed (the user can change some para-meters in the database with the View/Edit Parameters command). A New Simulationdatabase has to be compiled to convey changes from the model files. Editing of model files ispossible while a simulation is running. The parameter selection popup menus in simulationcommands depend however on the content of the source model definition files; they may notwork as expected if substantial changes were made to the model.

Simulation database status and available menusThe status of the simulation database determines which Simulation menu commands areenabled. Four alternatives are possible:- No simulation database is in memory or the preceding simulation was Closed. All the

Simulation menu commands are dimmed. Open Simulation or do New Simulation toget a simulation database.

Integration commands

Output commands

Experiment commands

Window commands

Figure IV/15: the Simulation menu when there is no simulation database in memory orthe preceding simulation was Closed.

- A “fresh” simulation database is in memory. This is a new simulation which has never Run.All Simulation menu commands are available. Experiment commands and the TextOutput command are marked if they are active. The Plot Window command can be used tomake it the frontmost window if necessary. The integration, output and experimentcommands are used to prepare the simulation. The Configure Plots command works onlyon “fresh” simulation databases.



Output commands

Experiment commands

Window commands

Figure IV/16: the Simulation menu when there is a “fresh” simulation database inmemory. Text Output and a Current Clamp experiment are active, thePlot Window and Time Window are shown.

- A “started” simulation database is in memory This simulation is Running (or has beenrunning but the user Paused). All Simulation menu commands are available. The Runcommand changes to Pause while the simulation is running. The Configure Plotcommand is italicized, Text Output is italicized if in use. The integration commands workonly during Pauses. All the experiment commands work, but be cautious with makingchanges to experiments while the simulation is running. Most changes to the experimentsettings will take effect from the next major time step on; retroactive changes are not possible.


Output commands

Experiment commands

Window commands

Figure IV/17: the Simulation menu while a simulation is Running. Text Output and aCurrent Clamp experiment are active, the Plot Window and TimeWindow are shown.

- An “old” simulation database is in memory The simulation has ended (the simulation timeequals the End time in the Integration Settings dialog). The window commands(Measure included) work. Other Simulation menu commands are available if they wereactive (marked) during the simulation, but italicized to show that one can use the commandsonly to view parameters and settings. Menu commands that were not used are dimmed. Thetitle of the plot window is added to the list at the bottom of the Simulation menu.

Because only one simulation database can be in memory one has to Close the simulation beforea New or Open Simulation command is possible. This can be done in two ways: Close“simulation window title” (default) closes the simulation database and all its windows; Closesimulation (press the option key while selecting the menu) closes the simulation database butkeeps the plot window (it is listed at the bottom of the Simulation menu).



Output commands

Experiment commands

Window commands

Old simulation windows

Figure IV/18: the Simulation menu when there is an “old” simulation database in memory.The simulation has not been closed yet, it had text output and a current clampexperiment.

Close simulation dims all Simulation menu commands and one can no longer access theresults or parameters (except for the graphic output if the plot window was not closed). The“old” simulation database remains however in memory and it can be the template for the nextsimulation database.

Selecting the model for a simulation databaseThe New Simulation command makes a new simulation database (Fig. IV/19), with asimulation number one higher than the preceding one. The content of the new simulation data-base can come from one of three sources.If an “old” database is in memory it can be used as a template for the new one by selecting theUse… option. This is fast because it requires no database compilation. The new simulationdatabase is identical to the preceding one. Use this option to run several experiments on thesame model or to repeat a simulation with other integration or output settings.

Figure IV/19: the New Simulation command dialog window.

If no “old” simulation database is available, or to run a simulation of a different model or of aslightly changed model, select a the Compile from… options with either a network definitionor neuron definition file as the source from which a new simulation database will be compiled.The available source files presented by the New Simulation dialog (Fig. IV/19) depend onwhat is in memory. Simulations can be compiled only if the source model file and all linkedfiles are loaded. Any changes to the source model definition files will be included in thesimulation database, even if they have not been Saved yet.


Compiling a new simulation database may take several seconds, depending on the number ofcompartments and subdefinitions in the model. After the compilation is finished the newsimulation database will conform completely to all the source definition files.The newly compiled database will inherit its integration and graphic output settings from thepreceding one, or it will have default settings if no simulation database was in memory. Inheri-tance of experiment and text output settings is under user control. If the Clear experiment &text output check box is switched on (Fig. IV/19) all these settings will return to their defaultvalues. The inheritance of simulation settings can be a powerful tool to create a simulation of anew model without having to specify all the settings. If the correct settings are in memory (forexample from a simulation data file after an Open Simulation) they will work on the newmodel also. If the new model is quite different from the preceding one, Nodus tries to apply thesettings to the new model using comparison techniques. The comparisons are based on theorder in which (sub)definitions were used and on hidden ID-numbers for compartments. Whenediting model files one should try not to change the order of the neurons in a network definitionfile, of the conductances in a Ionic Currents subdefinition or of the compartment ties toSynaptic Currents subdefinitions. The advantage of this approach is that when any of these(sub)definitions is replaced in a model by an analogue (for example a better conductanceequation), all simulation parameters settings will remain operational. A disadvantage is thatwhen a (sub)definitions is deleted, all settings referring to it or to subsequent (sub)definitionswill seem to shift. If Nodus does not succeed in applying the settings an alert listing all theproblems will warn the user, use the appropriate menu commands to correct these settings.

The initial value problemTo Run a simulation one needs initial values for all the parameters to start the computationsfrom. All the compartments in the neuron(s) have an initial membrane potential and if subdefi-nitions are used several other parameters also need initial values. The user has limited globalcontrol over initial values and full single value control. After Open Simulation the initialvalues are loaded from the simulation definition file; these values are the parameter valuespresent in the simulation database when it was Saved. The New Simulation dialog hasseveral options for automatic initial value generation (Fig. IV/19).Initial values can be extremely important to the success of a simulation, especially if voltagedependent ionic conductances are used in the model. If conductance (in)activation factors arenot matched initially the simulated neuron may behave irregularly (period of firing will bevariable, etc.) till a “dynamic equilibrium” is reached. In some cases the simulation may reachan “equilibrium” which has no biological meaning (for example a continuous depolarizationinstead of firing), caused by an initial imbalance in conductance (in)activation factors which wastoo large to get corrected spontaneously.The New Simulation dialog window (Fig. IV/19) presents up to three options for automaticgeneration of initial values. One of these options will always be selected as default, this can becontrolled with the Preferences menu. The Values in memory option is available only if an“old” simulation database is in memory.

Figure IV/20: error message during execution of a New Simulation command withValues in memory option for the Initial values. For some simulation para-meters no old values were available, they will be initialized with the Restingpotential option.


For complex models Values in memory is often the best option, in this way one may startwith an “equilibrated” simulation. The initial values can be loaded from a simulation data filewith Open Simulation; if one wants to simulate a slightly different model Close it again anddo a New Simulation of the desired model. If the new model is quite different from thepreceding one, Nodus tries to find initial values for all the model parameters using comparisontechniques. If Nodus cannot find some parameters in the “old” simulation database, it alerts theuser and uses the Resting potential option for these parameters (Fig. IV/20).

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Figure IV/20: the first 600 ms from the ‘Test-cell 2 Demo’ simulation. Upper axis: membranepotential in the second compartment (were the ionic currents are located).Middle axis: conductance activation (fat lines) and inactivation (thin lines) forthe ‘CS Fast Na Current’; full lines: “dynamic” values computed by thesimulation; broken lines: “static equilibrium” values (M∞ and N∞) for the mem-brane potential at that time. Lower axis: same for the ‘CS Delayed Rectifier’.


The Resting potential and Set to … mV work in similar ways; in the first case all com-partments are initialized to their specific resting membrane potential, otherwise they are all set tothe same (user specified) potential. All the other parameters are set at theoretical equilibriumvalues for the selected membrane potential. Note that this is a “static equilibrium” as opposed toa “dynamic equilibrium” that the simulation can settle in. Consider the firing neuron from the“equilibrated” ‘Test-cell 2 Demo’ simulation (Fig. IV/21). The conductance (in)activationfactors are changing in a smooth fashion and the membrane voltage is also changing contin-uously, as a result the (in)activation factors never reach their theoretical equilibrium value (M∞and N∞) though they approach it during the slow repolarization phase. The combination of allthese parameters at a precise time in the simulation constitute a “dynamic equilibrium”, whichusually cannot be reconstructed analytically.Initial values may also be changed manually with the View/Edit Parameters command afterthe simulation database has been compiled. This is not recommended for the inexperienced useras one can easily introduce combinations of parameters that produce unexpected results. Notealso that some parameters are stored in a derived format, see the description in the section on theSimulation menu in chapter V.In network models on going synaptic events may be considered initial values also. Synapticfiring times can be set explicitly at the postsynaptic sites with the corresponding command, butthe synapses cannot be set to have begun firing before the start of the simulation. The only wayto have on going synaptic events when the simulation starts is to have “old” synaptic eventsfrom a preceding simulation present. Usually one wants to Clear all synaptic events frompreceding simulations. This is the default option presented in the New Simulation and OpenSimulation dialog windows (Fig. IV/19 and Fig. IV/8), switch off this option to keep allsynaptic events from the preceding simulation. If the synaptic events are not cleared thesynapses in the new simulation database will behave as if the new simulation is a continuation intime of the preceding one.

Improving simulation speedMany factors determine the duration of a simulation. With complex models, having hundreds ofcompartments or a lot of ionic currents, simulation speed can become an important limitingfactor. An insight in what determines the simulation speed may prevent some problems. Somefactors have a predictable, (semi)linear effect on simulation speed, they are described ascorrelations. The effect of several other factors is highly non-linear and difficult to predictbecause it depends on how fast other parameters in the simulation are changing (“stiffness”, forexample electrotonic length effects and the change in membrane potential).Hardware and System factors:- A fast computer yields of course a better simulation speed. The Macintosh® computers on

which Nodus 3 can run ordered by decreasing speed are: IIfx >> IIci > IIcx = IIx ≈ SE30 > II(1990 status). Upgrading from any Mac to a Mac IIfx will improve simulation speed a lot, thedifference between the other Macs is in my opinion not significant (less than 30%).

- A completely dedicated computer is faster than one that shares time between different appli-cations. One can switch off the MultiFinder™ environment so that there is no time sharing onthe Macintosh, but most users do not want to do this too often (and it is impossible in System7). It helps however to Quit other applications if possible and to always run Nodus as theforeground application (so that its menu bar is shown). The use of virtual memory may alsoslow down Nodus because of frequent disk accesses. Simulation speed can be improved byan additional 10% (more if other applications are using the Mac) if the High multifinderpriority option in the Preferences dialog is switched on. This however significantly slowsdown all other activities on the Macintosh and may produce some unwanted side-effects (becareful on networks). It may also increase the initial menu interface response time.


Model dependent factors:- Simulation speed is inversely correlated with the total number of compartments in the neuron

model(s) and with the total number of subdefinitions. Ionic current subdefinitions or anyProcess dependent subdefinition are worse than others. See the ‘Making New Models’section for some tips on how to use different models for different experiments.

- A very short electrotonic length of any compartment in the model may have catastrophiceffects upon simulation speed (stiffness problem).

- For ionic currents simulation speed is inversely correlated with the number of (in)activationfactors. Fast ionic and synaptic conductance time constants can slow down the simulation.

- A large number of active synapses (synaptic conductance larger than zero) in a network slowsdown simulation speed. Note that variable transmitter release synapses are almost contin-uously active.

Integration dependent factors:- The accurate Fehlberg method is much slower than the hybrid Euler method. Use the

Fehlberg method only for passive membrane models or to check final simulation results priorto publication. In both methods increasing accuracy (by decreasing the value typed into theRelative error or Maximum ∆V box) can reduce the simulation speed a lot.

- The major time step usually has little influence on simulation speed, but if it is too small it maylimit the minor time step (because the minor time step can never be larger than the major timestep) and slow down computation. Very large major time steps may slow down simulationspeed when a lot of synapses are active in a network simulation.

- The use of a table with precalculated values for conductance (in)activation factors increasescomputation speed in hybrid method integration of excitable membrane models much. Thevoltage range of the table should include all excitable membrane potential values computedduring the simulation (otherwise the values have to be calculated repeatedly during the simu-lation). Do not make the voltage range larger than necessary; this increases the voltage stepfor the tabulated values with a loss of accuracy as consequence.

- Once a synapse postsynaptic to a constant transmitter release site starts firing, it does not stoptill the synaptic shut off time is reached (even when the conductance has become negligiblesmall). Network simulations with a lot of active synapses can speed up considerable if a goodsynaptic shut off time is selected. However do not make the shut off time too small; errorsmay be introduced because large synaptic conductances will be shut off suddenly.

- Swift changes caused by experiments (current steps, voltage clamps, firing of a synapse) canslow down the simulation when they occur, more so if the resulting change in membranevoltage has a large amplitude.

While simulations are running the behavior of Nodus is completely determined by the time re-quired to compute one minor time step (or several minor time steps if the.High multifinderpriority option in the Preferences dialog is switched on). If this takes more than a second(for example with large neuron models, models with a lot of excitable membrane, Fehlbergmethod, etc.) then the interactive response to user commands (mouse clicks, selecting a menucommand, etc.) will be sluggish. The same goes for other applications if Nodus is running inthe background under MultiFinder™. The only solution is to do nothing else on the computerduring the simulation.

Selecting Simulation ParametersAn important design goal of Nodus 3 was to have user friendly access to all variables, this wasrealized with popup menus. The selection of simulation parameters requires the combined useof several of popup menus and the user has some control over how these popup menus areorganized.


The selection of a simulation parameterMost simulation database parameters can be selected for graphic or text output (Fig. IV/22). Thelist of available simulation parameters includes values which are constant (like reversalpotentials) or which are not available (like concentrations) in Nodus 3.1. These are included forfuture compatibility. In later versions of Nodus concentrations will be implemented andchanging reversal potentials and maximum conductances will be possible.The first step in selecting simulation parameters for output is to choose the type of parameter.This is done with the value type selection popup menu (Fig. IV/22); this popup menu figures inthe View/Edit Parameters, Configure Plots and Text Output commands. Simulationparameters which are not present in the simulation database are dimmed.

Figure IV/22: the value type selection popup menu. With this menu a type of simulationparameter is selected for output.

Usually a lot of parameters of a single type are available; the user has to select a specificparameter for output. In all the experiment commands the user also has to select parameters: forthe Current Clamp and Voltage Clamp experiments compartments, for the SynapticFiring Times command postsynaptic sites and for Block Ionic Currents experimentscurrents. All these output and experiment parameters are selected with the “selection popuprow” (Fig. IV/23).

Neuron selectionCompartment preselection

Compartment selectionSubdefinition selection

ABC

Figure IV/23: three “selection popup rows”. In A a full set of 4 popup menus which deter-mine a synapse parameter. In B a set of 3 popup menus which determine amembrane potential parameter. In C a popup row for an output parameterthat is not in use.

The parameter selection popup menus are created from the data present in the source modeldefinition files. The selection popup rows function only if the source model definition files arepresent in memory and if they have not changed too much since the simulation database wascreated (see also the last subsection). The parameter selection process basically maps the modeldata structure to the simulation database structure.


Each selection popup row makes the unambiguous selection of a single simulation parameterpossible. Active selection popup rows consist of a set of 3 or 4 popup menus depending on theselected value type (Fig. IV/23). Parameters are selected from left to right, i.e. first select theappropriate item in the first popup menu then in the second one, etc. The neuron popup menuwill show either not used when nothing is selected or the local name of a neuron present in thesimulation model. Select the appropriate neuron, the compartment popup menu will change toshow the first (appropriate) compartment of that neuron. The compartment popup menu showsthe compartment number and its name if it has one, otherwise its structure type. Select thedesired compartment with the compartment popup menu (the subdefinition popup menu willchange). If the value type is a membrane potential the selection is complete, otherwise asubdefinition selection has to be made also. The subdefinition popup menu shows a memoryID-number (which is of no concern to the user) for the subdefinition and its name.The use of the compartment preselection popup menu is explained in the next subsection.The compartment popup menus are created selectively, except for experiment value types. Thecompartment popup menu will only show compartments were the selected value type is present;for example if ionic currents are selected only excitable compartments will be shown. Theneuron popup and compartment preselection popup menus are not selective, they show allavailable items. If the selected neuron or compartment preselection contain no appropriatecompartments an alert warning about an “empty selection” will be shown (Fig. IV/31); thepopup menus will revert to the previous setting.The selection popup rows for experiment value types are smaller. For injected currents theyare similar to membrane potential selection rows with 3 popup menus (Fig. IV/23B). For vol-tage clamps only the neuron popup menu is shown. For conductance blocking factorsthe first popup menu is an ionic current popup menu and is the only popup menu shown.The use of selection popup rows differs slightly between output commands (Fig. IV/24) andexperiment commands (Fig. IV/25). In output dialog windows several selection popup rows arepresent (Fig. IV/24); each row represents one parameter for graphic or text output. Theselection popup row can be active (it shows 3 or 4 popup menus that determine a simulationparameter) or not (it shows not used). The number of parameters that will be plotted or savedto the disk is determined by the number of active popup rows. Active and inactive selectionpopup rows may be interspersed, though this does not improve clarity!

Figure IV/24: the Configure Plots dialog window. Four selection popup rows are active,thus on the second axis four parameters will be plotted.


In experiment dialog windows there is only one selection popup row (Fig. IV/25), which isalways active. It is used to select the place (i.e. a compartment, postsynaptic site or currentdepending on the type of experiment) on which the experiment settings in the dialog windowwill operate. All experiment settings can be active at several places at once; for example currentscan be injected in several different compartments. In most experiment dialogs (except in theVoltage Clamp dialog) only one place in the model can be viewed; the Synaptic FiringTimes dialog shows all firing times at one synaptic site, the Current Clamp command asingle current injection in one compartment, etc. Use the selection popup row to select otherplaces in the model and view or edit the experiment settings there. Initially the dialog windowwill show a place in the model where a setting is active or the first available place in the model ifno experiment settings are active. All experiment commands have a button to Delete all thesettings at all places in the model.

Figure IV/25: the Synaptic Firing Times dialog window. Two firing times are preset atthe postsynaptic site shown in the selection popup row; other firing timesmight be preset at other postsynaptic sites.

The compartment preselection popup menuPopup menus are a nice instrument to select parameters, but they should not be too long.Imagine selecting a compartment for membrane potential output in a 1000-compartments neuronmodel, a compartment popup menu showing all 1000 compartments would be hard to use!Therefore Nodus never shows more than 50 items in a popup menu. To select which set of 50compartments in a 1000-compartment model will be shown in the compartment popup menu, thecompartment preselection popup menu is used.The compartment preselection popup menu is the second popup menu in the selection popuprow (Fig. IV/27). It makes selection of a subrange in the compartment popup menu possible.The user has control over the compartment preselection process with 2 options in thePreferences command.The Select by structure type option in the Preferences dialog window forces Nodus toalways display a structure type preselection popup menu (Fig. IV/26), corresponding to theStructure type popup menu in the compartment dialog window (Fig. IV/13) (with an allcomps menu item added). The compartment popup menu will then only show compartmentswith the corresponding structure and where the requested value type is present. The all compsmenu item will be dimmed for large neuron models. This option is useful if (most) compart-ments in the model have a structure type different from undefined. Note that in very largeneuron models more than 50 compartments may have the same structure type.The Show only named comparts option in the Preferences dialog window affects thecompartment popup menu directly. Only compartments which have a compartment name (Fig.IV/13) will be shown, unnamed compartments cannot be selected. This option is useful ifcompartments were named selectively, so that only “interesting” ones are shown. The Showonly named comparts option may be combined with the Select by structure type option.


Figure IV/26: Compartment preselection by structure type. The complete structure typepreselection popup menu is shown at the left. At the right the effect of astructure type preselection upon the compartment popup menu is shown in amembrane potential selection popup row.

The appearance of the compartment preselection popup menu depends on the size of the selectedneuron model and the Select by structure type option (Fig. IV/27). If the neuron model issmall and no structure type selection is requested, the words all comps will be shown insteadof a preselection popup menu (Fig. IV/24). If the neuron model is large and no structure typeselection is requested, a range preselection popup menu is shown (Fig. IV/27). The rangepreselection popup menu just contains subranges of compartment numbers, i.e. #1-#50, #51-#52, etc. If the Select by structure type option is on, there will always be a structure typepreselection popup menu. The size of the neuron model determines whether the all compsmenu item is available or not.

5-compartment neuron: ionic current selection

109-compartment neuron:membrane potential selection

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popup menu

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Figure IV/27: four types of preselection popup menus are possible, depending on the sizeof the neuron model and the Select by structure type option.

Error messages about parameter selectionA simulation parameter selection maps the model data structure to the simulation databasestructure. This process will work fine if the model and database structures coincide, otherwiseNodus will warn the user that there is a problem. Which error message is shown depends onthe circumstances.Errors during New SimulationA new simulation database inherits its output and experiment settings from the “old” simulationdatabase (if present, otherwise defaults are used). If the model used for the new simulation isquite different from the preceding one, some output and/or experiment settings may no longer berelevant.


For example graphic output of ionic currents will not work very well for a passive membranemodel. Nodus warns that some simulation parameters could not be found and lists them all inthe error message (Fig. IV/28)

Figure IV/28: error message during execution of a New Simulation command with a newsource definition file. The new model is too different from the preceding one;some simulation parameters no longer exist.

Correct the listed settings before running the simulation by executing the appropriate simulationoutput and/or experiment commands. When such an output or experiment command is activatedan additional error message will be shown (Fig IV/30). If the settings are not corrected thesimulation can run, but the involved output will be continuously zero and the concerned experi-ments will not work.Errors during simulation output or experiment commandsThe selection popup row in all simulation output and experiment dialogs is created from data inthe source model definition files, which have to be in memory. If the source files are not inmemory, Nodus will warn the user (Fig. IV/29). This condition can occur only after the userhas Closed some definition windows or after an Open Simulation with the Open alllinked files check box switched off (Fig. IV/8).

Figure IV/29: error message warning that some source model definition files are not inmemory. This message will displayed when a View/Edit Parameters,Configure Plots, Text Output, Current Clamp, Voltage Clamp,Synaptic Firing Times or Block Ionic Currents command is executed.

The best solution is to press Cancel and Open the required source model definition files beforeexecuting the required command again. For output commands one can Continue. AfterContinue the output dialog windows will show the raw parameter selection numbers thatNodus uses internally. These selection numbers are not documented, this option is available foremergency use only. The average user should never edit the raw parameter selection numbers!Experiment commands are not accessible; the Continue button is replaced with a Deleteexperiment settings button.which clears the experiment completely when pressed

If the source model definition files are in memory, but have been edited since the compilation ofthe simulation database, some of the parameter settings may have become invalid. The changesin definition files causing this problem are usually deletions: deleting a compartment (FuseCompartments), removing a compartment “tie” to a subdefinition or removing a neuron in anetwork. Any output or experiment setting referring to the deleted compartment or subdefinitionhas then become irrelevant. Deletions can cause two problems: either simulation parameters areno longer available and an error message (Fig. IV/30) will be shown or some settings will seemto have “shifted” and point to different simulation parameters (see ‘The initial value problem’).


Figure IV/30: error message displayed upon selecting an output or experiment command.

Discrepancies between the simulation database and the (edited) sourcemodel definition files make the display of a parameter selection popup rowimpossible.

Simulations will run without problems because the simulation database has not changed, but theparameter selection popup row can no longer display the correct popup menu item. Noduswarns the user which model structure was not found before displaying the simulation output orexperiment command (Fig. IV/30). To prevent this problem one should keep original sourcemodel definition files, rename the file (and give it a new hidden ID-number) after large changesto the model structure by doing Save As.If Continue is pressed the raw parameter selection numbers that Nodus uses internally will beshown for those parameters that are no longer present in the source model files. Do not try toedit these parameter selection numbers, but change any other parameter or popup menu as youlike. The Continue & delete concerned settings button is useful when this happened aftera New Simulation database compilation. As the concerned parameters are no longer in thesimulation database either one has to change or delete them anyway. All parameter selectionsthat are no longer relevant will be replaced by the not used setting, the other selections remainunchanged.

During popup menu parameter selections one may try to select a neuron or compartment pre-selection menu item that has no compartments with the requested value type. This could happenfor example in a network simulation, when a synaptic current value type is selected and the userselects with the neuron popup menu (Fig. IV/23) a neuron that has only presynaptic sites. Thenan alert like in Fig. IV/31 will be shown and the popup menus will revert to their previoussetting. This may happen frequently if the Select by structure type option is active.

Figure IV/31: error message warning that the selected neuron or compartment pre-selection popup menu item cannot be used for the requested value andstructure type (in this case synaptic currents and neurites).


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V. NODUS MENU COMMANDS

Apple Menu 5 5File Menu 5 5Edit Menu 6 2Simulation Menu 6 4

Windows and cursors............................................................... 64Simulation value types.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66Menu commands .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Network Menu 8 0Windows ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Menu Commands.................................................................... 81

Neuron Menu 8 3Windows ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Menu Commands.................................................................... 87

Conductance Menu 9 9Windows ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Menu Commands................................................................... 100

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Apple Menu

About Nodus…The About Nodus dialog window shows the Nodus version and the available free heap memoryin kilobytes (K) and % of the total heap memory. Note that the global memory containing all thedefinition data and the simulation database is not included in this count (see chapter II).Available memory should be higher than 100K, if it gets below 80K Nodus will alert the userwhenever a menu command is invoked.

Figure V/1: the About Nodus dialog window.

File MenuThe Nodus menu is similar to the File menu in most Macintosh® applications. It contains all themenu commands used to manage Nodus files. Some menu items contain submenus, see chapterIV and Fig. IV/2; submenus are listed here as separate menu commands.

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Figure V/2: the File menu.

New Simulation…This menu command is enabled if there is no simulation database in memory and if an “old”database is still available and/or a network definition file or neuron definition file is in memory.

Figure V/3: the New Simulation command dialog window.

The New Simulation command dialog window contains 2 section: one specifying the sourcemodel definition file for the New simulation database and one setting the Initial values.Refer to chapter IV, Fig. IV/19 for a more extensive discussion on simulation database compi-lation and the initial value problem. Press OK to create a new simulation database.Simulation database:The new simulation database can be either:

Use ‘old simulation database’: an exact copy of the simulation database used in thepreceding simulation.Compile from ‘network definition file’: a new simulation database is compiled fromthe network definition, with all the parameters taken from the network definition window orfrom memory if the window is hidden.Compile from ‘neuron definition file’: a new simulation database is compiled from theneuron definition, which can be selected with a popup menu. All parameters are taken fromthe neuron definition window or from memory if the window is hidden.

Initial values:Values in memory: all integrated simulation parameters are taken from the “old” simulationdatabase. Values which cannot be found are initialized to equilibrium values for restingpotential.Resting potential: all compartmental membrane voltages are set to their neurons restingmembrane potential. Other integrated values are put at equilibrium values for resting potential.

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Set to…mV: all compartmental membrane voltages are set to the user specified membranepotential. Other integrated values are put at equilibrium values for this potential.

Which option is preselected depends on whether an “old” simulation database exists and on thesettings in the Preferences command.Additional initial values options are:

Clear all synaptic events: any synaptic events still in memory from preceding simulationswill be cleared.Clear experiments & text output: all experiments and text output settings in memory aredisabled. No experiments or text output will be active.

New SimulationThis menu command is available when the shift key is pressed. No dialog window will beshown if there is an “old” simulation database in memory; the Use ‘old simulation data-base’ and Values in memory options are used.

New Network…This menu command is enabled if there is no network definition file in memory and if at leastone neuron definition file is in memory.The New Network command creates an empty network definition window (Fig. IV/14) called‘Untitled Network’. See the ‘Network Menu’ section for a description of the window contents.

New Neuron…This menu command is always enabled (unless 20 neuron definition files are in memory!).The New Neuron command creates a default neuron definition window (Fig. IV/12) called‘Untitled Neuron #’. See the ‘Neuron Menu’ section for a description of the window contents.

New Conductance…This menu command is always enabled (unless 20 conductance definition files are in memory!).The New Conductance command creates an empty conductance definition window (Fig. IV/11)called ‘Untitled Conductance #’. See the ‘Conductance Menu’ section for a description of thewindow contents.

Open Simulation…This menu command is enabled if there is no simulation database in memory.

Figure V/4: the Open Simulation file dialog window.

A standard minifinder dialog window is shown that lets the user select a simulation data file toOpen. A Simulation Plot Window will be displayed.Options:

Clear all synaptic events: any synaptic events present in the simulation data file will becleared.

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Open all linked files: the source model definition file and all files linked to it are openedalso (unless they are already in memory). See the ‘Nodus Files’ section in chapter IV formore details. This option should always be used; the simulation output and experimentcommands will not work properly if the source model definition files are not in memory.

Open Network…This menu command is enabled if there is no network definition file in memory.

Figure V/5: the Open Network file dialog window.

A standard minifinder dialog window is shown that lets the user select a network definition fileto Open. A network definition window (Fig. V/30) will be displayed. See the Network menusection for a description of the window contents.Options:

Open all linked files: all the neuron definition files necessary for the network and all fileslinked to them are opened also. See the ‘Nodus Files’ section in chapter IV for more details.This option should always be used. A network definition cannot be loaded into memory ifone of its neuron definition files is not in memory.

Open Neuron…This menu command is always enabled (unless 20 neuron definition files are in memory!).

Figure V/6: the Open Neuron file dialog window.

A standard minifinder dialog window is shown that lets the user select a neuron definition file toOpen. A neuron definition window (Fig. V/33) will be displayed. See the Neuron menusection for a description of the window contents.Options:

Open all linked files: all the conductance definition files necessary for the neuron areopened also. See the ‘Nodus Files’ section in chapter IV for more details. This option shouldalways be used.

Open Conductance…This menu command is always enabled (unless 20 conductance definition files are in memory!).

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Figure V/7: the Open Conductance file dialog window.

A standard minifinder dialog window is shown that lets the user select a conductance definitionfile to Open. A conductance definition window (Fig. V/52) will be displayed. See theConductance menu section for a description of the window contents.There are no options.

Import Simulation…This command is not implemented in Nodus 3.1.

Import Neuron…This menu command is always enabled (unless 20 neuron definition files are in memory!).

Figure V/8: the Import Neuron file dialog window. At right the File format popupmenu.

A standard minifinder dialog window is shown that lets the user selects a standard Macintosh®text file containing morphological data to Open. The contents of this morphology file areinterpreted by Nodus and converted into a neuron definition. A default neuron definitionwindow (Fig. V/12) will be displayed. See the Neuron menu section for a description of thewindow contents.Options:

File format: this popup menu specifies the format of the morphological data. These formatsare defined in the ‘Appendix’. The Import Neuron command will not work if a wrong fileformat was selected.Keep 3-dimensional coordinates: the 3-dim coordinates present in the file are stored intothe neuron definition file. This option has no advantages in Nodus 3.1, it reduces themaximum number of compartments from 4000 to 3000.Automatic compartment names: compartment names are generated automatically. Thesenames reflect morphological information, like branching order. Do not use this option if theSelect only named comparts option in the Preferences dialog is set, because allcompartments will be named.

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Close ‘a window’Is enabled if any window is displayed.If it is a definition window, its contents are checked and stored in memory if no errors werefound. If any parameter value in the window is not acceptable, Nodus will alert the user and thewindow will not be closed.If the contents of the window were changed and these changes were not Saved, Nodus willalert the user before closing the window (if the appropriate Warnings option is active in thePreferences dialog).

Figure V/9: Warning shown before a window is Closed that has been edited, but notSaved. Press Yes to save the changes and close the window; No to close itwithout saving; if Cancel is pressed the window is not closed and not saved.

The action of the Close command depends on whether the window belongs to a linked file ornot. If it is a file linked to other files in memory (see the ‘Nodus Files’ section in chapter IV formore details), the window will be hidden. Otherwise the window will be closed and all its datawill be removed from memory.

Hide ‘a window’This command is available when the shift key is pressed and if any window is displayed.If it is a definition window, its contents are checked and stored in memory if no errors werefound. If any parameter value in the window is not acceptable, Nodus will alert the user and thewindow will not be hidden.The window data remain in memory after the window is hidden. The window can be displayedagain by selecting the menu item with the same title at the bottom of the appropriate menu.

Kill ‘a window’The command is available when the shift and the option key are pressed while a window ispresent.Kills the window, i.e. the window is closed without checking it contents. Any changes made tothe window contents are not stored. This command is useful to quickly close a window that hasseveral bad parameter values in it.

Close AllIs enabled if any window is displayed.Closes all Nodus windows in the same way as consecutive Close ‘a window’ commands.Refer to Close ‘a window’ for more details.

Hide AllThis command is available when the shift key is pressed and if any window is displayed.Hides all Nodus windows in the same way as consecutive Hide ‘a window’ commands.Refer to Hide ‘a window’ for more details.

Close All GraphsThis command is available when the option key is pressed and if any window is displayed.Closes all graphic windows, i.e. old simulation plots (not the active Plot Window), NeuronDiagrams and Conductance Plots. There is no option to save these windows first.

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Save ‘a window’Is enabled if any definition or simulation window is displayed.The window contents are checked and stored in memory if no errors were found. If any para-meter value in the window is not acceptable, Nodus will alert the user and nothing will be saved.The contents of the corresponding definition file are updated. The name and hidden ID-numberare not changed (see Fig. IV/6). If the file has never been Saved before (still called ‘Untitled’)the Save As dialog (Fig. V/10) is presented.

Save As…Is enabled if any definition or simulation window is displayed.

Figure V/10: the Save As file dialog window for conductance (left) and neuron (center)definition files. At right the File format popup menu.

Presents the standard minifinder dialog window which lets the user supply a new name and/orfolder location for the file. Press Save to confirm the command. A new file will be created; itwill have the new name and a new hidden ID-number. The old file is not changed.Options:

Update all links in memory: all links in hierarchically higher files in memory that point tothis file are updated to link to the new file instead of the old file. See the ‘Nodus Files’ sectionin chapter IV and Fig. IV/6 for more details.File format (neuron definition files only): neurons can either be saved in the StandardNodus format (default, this is the only format that can be Opened) or some of the data can bewritten to a text file. A text file can be either an Import Neuron File Format (Ladder orGenesis, see the ‘Appendix’) or it can be similar to printed output (Text).

Page Setup…Presents the standard Macintosh® page setup dialog, specific for the active printer. Use thiscommand to specify the page size and any reduction or enlargement. The settings are kept inmemory till Nodus quits. The 50% Reduction option is very useful if the Double sizesimulation plots option in the Preferences dialog is selected.

Print…Is enabled if any definition or graphics window is displayed.The standard Macintosh® print dialog, specific for the active printer, is presented so that theuser can select the number of copies, the quality of printing, etc. The contents of the windoware printed, the date and the window title are printed in a header. If it is a graphics window thegraphic is printed as it is displayed. Do not use draft printing for graphics! If it is a definitionwindow the complete definition is printed, using the Print font specified in the Preferencesdialog. Note that printing of large neuron definition files may take a lot of time. No dialog isshown during the preparation or the actual printing. Actual printing can be interrupted bypressing command-period.

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Automatic SavingThe Automatic Saving feature makes Nodus save the simulation database with all integration,output and experiment settings and the simulation results (including the Plot Window) in theNodus Resume file on Quitting. When Nodus is restarted, the application will look for this fileand load all data so that Nodus behaves as if it had never been interrupted. An instructionwindow is displayed if a simulation database present:

Figure V/11: the Automatic Saving instruction window.

This command is a toggle: a check mark is shown when the automatic loading/saving feature ison, select the command again to turn it off.The automatic command saves only the simulation database, definition files are not saved auto-matically (but the appropriate warnings will be displayed before Quitting).

QuitWhen this command is selected Nodus closes all windows and output files, writes a NodusResume file if the Automatic Saving command is on, and returns to the Finder.Nodus will check whether all definitions and the simulation database have been saved correctlyif the appropriate Warnings option is active in the Preferences dialog. An alert will warn theuser if any data have not been saved correctly, see Fig. V/8 (the Cancel button interrupts theQuit command).

Edit MenuThe Edit menu has a limited function in this Nodus version. It is used to transfer graphics toother applications and it is used by desk accessories.

Figure V/12: the Edit menu.

CopyThe only Nodus specific edit command is enabled when a graphics window (i.e. the Simu-lation Plot Window , an old simulation plot, a Neuron Diagram or a Conductance Plot) is theactive window.It copies the graphic to the Clipboard in PICT format. The graphic can then be pasted in theScrapbook or in other application windows (like MacWrite™ or MacDraw™).

Undo, Cut, Paste, ClearThese commands work only with some desk accessories. Refer to ‘Macintosh, the ownersguide’ for further information.

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PreferencesThis command presents a dialog window to let the user change several default settings inNodus. These settings are stored in the Nodus Preferences file.

Figure V/13: the Preferences dialog window.

General options:Tile windows: all definitions (except alerts) are tiled from the top, left of the screen to thebottom, right . Otherwise the windows are centered on the screen. Default is: on.High multifinder priority: Nodus gives the running simulation top priority for computertime while Nodus is running in the foreground. Simulations can compute up to 3 secondsbefore any other activity is allowed. This means that other applications running in thebackground will almost stop, which may be unacceptable (for example network activities orprint spooling). The response of Nodus to mouse clicks may also be sluggish if no menucommands have been executed for some time. Default is: off.Print font: selects the font used for printing of definitions and for text in graphics windows.Printing works best if Geneva or Helvetica is selected. If a Postscript™ laser printer is used,an appropriate font like Helvetica should be selected. Default is: Geneva.

Simulation options:Automatic loading/saving: this is identical to the Automatic Saving option in the Filemenu. Default is: off.Beep when finished: the computer beeps when the simulation has finished running.Default is: on.Quit when finished: Nodus Quits when the simulation has finished running. This may befriendly to other users in a multi-user environment. Default is: off.Double size simulation plots: new simulation plots are drawn in double resolution.They will not fit on most Macintosh® screens, but they will look nicer if printed at 50%Reduction (Page Setup dialog in the File menu). Default is: off.Current plots: negative up: all ionic and synaptic currents will be plotted with outwardcurrents (negative values) going up and inward currents (positive values) going down. Thisis the “voltage clamp” display that may be more familiar to electrophysiologists than thestandard display used by Nodus. Default is: off.Default to values in memory, Default to resting potential, Default to setpotential: selects which initial values option will be the default in the New Simulationdialog window (Fig. IV/19). Default is: Default to resting potential.Reset simulation numbers: resets the simulation number counter to zero. Cannot be un-done.

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Warnings options:Before closing definition file: Nodus will display a warning when any definitionwindow that has been changed without saving is going to be Closed. Default is: on.Before closing simulation file: Nodus will display a warning when a Simulation PlotWindow of a simulation database that has been changed without saving is going to beClosed. Default is: off.

Neuron definition options:Multiple use of subdefinitions: subdefinitions may be used by more than one neurondefinition file. See the ‘Nodus Files’ section in chapter IV for more details. Default is: off.Only node connections: the branch connection and weight factors options for linksbetween compartments are not displayed in compartment definition dialogs. Not available inNodus 3.1. Default is: off.Select by structure type: the compartment preselection popup menu in selection popuprows will always be a compartment structure type preselection popup menu. See ‘SelectingSimulation Parameters’ in chapter IV for more details. Default is: off.

Show only named comparts: the compartment popup menu in selection popup rowsshows only compartments that have a compartment name. See ‘Selecting SimulationParameters’ in chapter IV for more details. Default is: off.

Conductance options:in nS, in mS/cm2: all Gmax values in new Ionic Currents or Synaptic Currents sub-definitions will be specified in the selected unit. The setting in existing subdefinitions cannotbe changed. Default is: in mS/cm2.

Simulation Menu

Windows and cursorsPlot WindowThe simulation results are always plotted in a Plot Window. The plot settings are undercomplete user control with the Configure Plots command, which can only be used before thesimulation has Run. Plots are not drawn continuously and may look “staggered”; this isbecause Nodus tries to draw complete lines instead of individual points (see the Update Plotscommand for more details).

Figure V/12: the Plot Window.

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The Plot Window has a grow box and scroll bars. The grow box is always active, but the scrollbars remain dimmed till the simulation has finished running.Like all simulation windows, the Plot Window enables the Simulation menus. Closing thePlot Window is equivalent to closing the simulation database. The simulation database can beclosed however without closing the Plot Window: press the option key while clicking in theclose box or executing the Close simulation command. The Plot Window becomes a graphicwindow. It remains listed at the bottom of the Simulation menu, but will no longer enableSimulation menu commands.Time WindowThe Time Window shows the simulation time in milliseconds. This time will change continu-ously while the simulation is running.

Figure V/13: the Time Window.

Like all simulation windows, the Time Window enables the Simulation menus. The TimeWindow is optional, it may be closed without affecting the simulation database status. Justselect the corresponding command in the Simulation menu to get a new Time Window.Measure WindowThe Measure Window shows the position of the crosshair cursor in the Plot Window,transformed in time and value measurements in the correct units (Figs. V/14 and V/29).

Figure V/14: the Measure Window.

Each plot in the Plot Window has its own (rectangular) measuring region. If more than one plotis drawn then the values and units shown in the Measure Window will depend on the scaling ofthe axis in the measuring region, were the cursor is located. If the cursor is outside the PlotWindow, the display in the Measure Window does not change anymore.If the mouse button remains pressed and the cursor is dragged over the Plot Window theMeasure Window will show the differences between begin and end point of the cursor move-ment and the slope of the curve. Drag the cursor to measure period of firing, size of an actionpotential, rising slope of an IPSP, etc.Like all simulation windows, the Measure Window enables the Simulation menus. Click theclose box of the Measure Window to stop measuring, this will not affect the simulation databasestatus.Status WindowThe Status Window shows information about the actual simulation computations. It can be usedto time total computation time or to try to locate ‘trouble spots’ in a simulation model that slowdown computation.

Started on: date and time on which the simulation was started.Integration method: hybrid or Fehlberg method.Computation time: total computation time in minutes till now. Only actual computationtime is counted, Pausing or interrupting the simulation will not affect the computation time(except the time necessary to generate a new equation table in the hybrid method).Computation speed: the average computation speed in simulated milliseconds versus realtime minutes.

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Figure V/15: the Status Window.

Relative error/Maximum ∆V: actual maximum relative error (Fehlberg method) ormaximum change in membrane potential (hybrid method) used by the integration routines.Estimated error: the estimated error will only be shown if the simulation is Running. Whatis shown depends on the integration method chosen. For the hybrid method it will be thelargest change in membrane potential during the last integration step, for the Fehlberg methodit will be the estimated relative error. If the estimated error is shown bold the integration stephas failed, the minor time step will be decreased and a new attempt at integration will start. Ifit is shown plain the integration step succeeded. The simulation database array index at whichthe largest error occurred is shown.Minor time step: the actual minor time step as determined by the integration routines.

Like all simulation windows, the Status Window enables the Simulation menus. Its slowsdown simulations a lot, so use it only when needed. Closing the Status Window does not affectthe simulation database status.

Run cursorWhile a simulation is running, the cursor changes to the run cursor: a small oscilloscope. Two‘control lights’ indicate which integration method is being used (see Integration Settings).The left, red light burns when the hybrid method is used; the right, blue light burns when theFehlberg method is used.

Figure IV/16: the run cursor. Left: hybrid method used, right: Fehlberg method used.

Use the center of the oscilloscope screen to point with the run cursor.The appearance of the run cursor does not depend on what type of window is active.Crosshair cursorThe crosshair cursor is shown when measuring is active (Measure Window).

Figure IV/17: the crosshair cursor.

The center of the crosshair is the point being measured.

Simulation value typesTo select parameters for output (in the View/Edit Parameters, Configure Plots and TextOutput commands) one has to specify the type of value with a popup menu (Fig. V/18,‘Selecting Simulation Parameters’ in chapter IV). Some of these value types are not the originalmodel parameters, they have been optimized for simulation speed. The exact definition of eachvalue type follows, refer to chapter III for more information about the equations defining thesevariables.

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Figure V/18: the value type selection popup menu.

- membrane potential: E (Eq. 4, Eq. 28).- conductance (in)activation: M or H (Eq. 21).

- conductance time constant: τM or τH (Eq. 23).- ionic conductance: G(E,t) (Eq. 24-26).- maximum conductance: derived from Gmax (Eq. 24-26). Has been scaled for membrane

surface (if it was in mS/cm2).- reversal potential: Ek (Eq. 27).- ionic current: Ik (Eq. 27).- transmitter release: if variable: Ts (Eq. 29-31); if constant a derived value: -b (synapse is not

firing) or b (synapse is firing).- synaptic conductance: Gs (Eq. 32-36).- synaptic peak conductance: derived from Gmax (Eq. 32-36). Has been scaled for membrane

surface (if it was in mS/cm2). For dual exponential (Eq. 34) functions it contains the normal-izing factor for the variable conductance.

- synaptic reversal potential: Es (Eq. 37).- synaptic current: Is (Eq. 37).- concentration: the concentration in its local units (not available)- in/out process: the diffusion, buffering, etc process in its local units (not available).- injected current: Ie (Eq. 4, Eq 28).- voltage clamp current: Iv = -CMn∂E/dt .- conductance blocking factor: varies between 0 and 1; 1 is no block, 0 is complete block.

Menu commandsThe File menu commands are active when a simulation window is the highlighted window.Several File menu commands can be italicized (Chapter IV) when the simulation has started orfinished running. This means that the command will show a dialog window that can be viewed,but not edited. The user can then examine the integration, output and experiment settings but notchange them (the simulation has finished!).

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Figure IV/19: the Simulation menu

RunThe command is enabled when a simulation database is in memory that has not yet finishedrunning.Starts or continues the simulation and the command name changes to Pause. The cursorchanges to the run cursor (a small oscilloscope) while the simulation is running. If the hybridEuler method is selected (Integration Settings), a equation table will be created first and adialog window will show progress information. See ‘Making New Simulations’ in chapter IVand Fig. IV/17) for more details on how the Run command affects the Simulation menu.Running the simulation is an iterative process: the integration routine calculates the membranepotential and concentrations in all compartments and all other relevant parameters and tests if theresults meet the accuracy requirements. If the test fails the minor time step is decreased and theintegration routine tries again (this process can be followed in the Status Window). If theintegration step is successful the simulation time is increased by the minor time step (visible inthe Time Window) and plot data are send to the Plot Window; then another integration step isstarted unless the simulation is finished.

PauseWhile a simulation is running the Run command changes into the Pause command.Pauses the simulation and the command becomes Run again. Pausing the simulation haslimited use. It is the only way to access the Integration Settings command after the simu-lation has started. One can also Pause a simulation if Nodus is interfering too much with otherapplications which are running under the MultFinder™, continue Running when the otherapplications have finished.

Integration Setting s …The command is enabled if the simulation has never Run or during Pauses. It is italicizedwhen the simulation has finished running.This command gives control over the integration procedures used by Nodus to calculate thechanges in membrane potential and concentrations in the compartments. The settings can bechanged any time before or after a simulation has started, usually the integration method will notbe changed during a simulation.Time controlsSet the duration and the minimum number of time steps for the simulation:

Begin: time in milliseconds at which the simulation will start, or the current time after asimulation has started. The default setting is 0 ms, most users will never change this value.

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End: time in milliseconds at which the simulation will stop. This important value sets thesimulation length, it depends on the experiment that is being simulated (default: 1000 ms).Major time step: the integration routines are forced to adapt the minor time steps so thateach major time step is reached exactly. Experiments can start or stop only at major time stepsand Text Output may also be controlled by the major time step. See ‘Integration Methods’in chapter III and ‘Making New Simulations’ in chapter IV for more details on minor andmajor time steps. The default setting is 1 ms.Fixed time step: integration will be performed with a fixed time step, equal to the majortime step. The integration methods normally use variable time steps, which is more efficient.In exceptional situations the step adaptation routines may fail and a fixed time step has to beused. Note that Relative error/Maximum ∆V checking will have no effect, the simulationwill fail when membrane potential exceeds ±200 mV.

Synaptic currentsShut off after: the synaptic shut off time. Synaptic conductances controlled by alpha ordual exponential functions will be switched off after the specified time. See ‘Making NewSimulations’ in chapter IV for more details. The default setting is 1000 ms.

Figure V/20: the Integration Settings dialog window. Left: hybrid Euler method isselected, right: Fehlberg method is selected.

Integration methodControls which integration method will be used.

Hybrid Euler method: a fast but relatively inaccurate integration method.Maximum ∆V: this value controls the accuracy of the hybrid method. It sets the maximumamount by which the membrane potential may change in any compartment during one minortime step. The integration routines will decrease the minor time step till this criterion isfulfilled or give an error message if the minor time steps gets too small. Default is 0.1 mV.Maximum […]: additional control of the accuracy of the hybrid method. Is shown whenconcentrations are present in the model. Sets the maximum amount by which any concen-tration may change during one minor time step.Equation table from … to … mV: to increase computation speed a table of conductance(in)activation factors is calculated before the simulation starts. The range that is precalculatedis determined by this setting, outside this range the (in)activation factors have to be computedduring the simulation. See ‘Integration Methods’ in chapter III for more details. Default isfrom -80 to 45 mV

Fehlberg method is a fifth order Runge-Kutta method, it can be very accurate (up to10-12), but is sometimes slow. It is quite sensitive to the stiffness of the differential equa-tions.

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Relative error: this value controls the accuracy of the Fehlberg method. It sets themaximum estimated relative error during the integration of membrane potentials, concen-trations, variable transmitter release, synaptic conductances and ionic conductance (in)acti-vation factors in any compartment during a minor time step. The integration routines willdecrease the minor time step till this criterion is fulfilled.

View/Edit Parameters…The command is always enabled if a simulation database exists. It is italicized when the simu-lation has finished running.

Figure V/21: the View/Edit Parameters dialog window.

With this command one can view most of the simulation database parameters. One can also editthe simulation parameters to change their (initial) values, but be very careful with this option.Editing membrane potentials is easy, editing anything else may produce unexpected results!Parameters selection popup row:The box in the upper part of the dialog window contains a value type selection popup menu(Fig. V/18) and a parameter selection popup row (see ‘Selecting Simulation Parameters’ chapterIV). These allow selection of one simulation parameter to view or edit.Value:

Value: the value of the selected simulation parameter.∂V/∂t: the differential of the selected simulation parameter. Is not available for all para-meters.Allow editing: check this option to be able to edit the selected parameter, but beware! Anychanges are stored in the simulation database when OK is pressed or another simulationparameter is selected with the popup row.

Configure Plots…The command is always enabled if a simulation database exists. It is italicized once the simu-lation has started running.This command gives a choice between 7 kinds of plotting configurations, with up to 4 separateaxes and a maximum of 20 plotted parameters. Configure the plot settings before running thesimulation, afterwards the settings cannot be changed anymore.Axis selectionThe left upper corner contains 7 plot configuration icons. The configurations differ in thenumber and position of the plot axes available. Click on one of the icons to select it, it will behighlighted.

Edit axis #: these push buttons controls which axis data are shown in the lower part of thedialog. Press a button to change the displayed axis data, any changes made to the presentlyshown axis data will be stored in memory.

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Figure V/22: the Configure Plots dialog window.

Plot optionsThe upper right part of the dialog contains some options which affect all axes.

Draw axes: if this option is checked time and value axes are drawn for all the plots (default).The time and value axes cross if zero is included in the value range, else a hanging time axis isdrawn. If this option is not checked a scale marker is placed at the lower right corner forevery plot.Repetitive sweeps: the graphic output is repeated in the same plotting areas in sweeps.When the end of the first Time axis is reached, all plots continue at the start of their TimeAxis. All plots are drawn in the same color (on color screens), the color changes for eachnew sweep. This option is particularly useful for Voltage Clamp experiments.1 time axis range: all displayed axes will have the same Time Axis range. Changing theTime Axis range for one axis, automatically changes it for all other axes also. This is whatone usually wants; all plots are aligned in time.

Axis data:The lower part of the dialog contains the data about one axis, its number is shown at the top.Press the Edit axis # buttons above to change the displayed axis data.

Time axis: enter the begin and end of the time axis in the from…to… text boxes (default isthe total simulation time). The time unit can be changed with the unit popup menu. The timelimits set in Integration Settings can be exceeded, this can be used to have the same scalefor graphic output of simulations with different total simulation times. The time scale can bechanged to enlarge significant events, etc.Value axis: select the displayed value type with the value type popup menu (Fig. V/18).Each axis can display only one value type. Enter the begin and end of the value axis in thefrom…to… text boxes. The value unit (which is specific for the selected value type) can bechanged with the unit popup menu. Choose values which are slightly larger than the maximaand minima reached by the plotted compartments so that the plots are 'filled’. Significantevents can be enlarged by choosing smaller value ranges, but a part of the plot will be lost.

Below the axis data are 5 parameter selection popup rows (see ‘Selecting Simulation Parameters’in chapter IV) which are used to specify which parameters will be plotted. Each row corre-sponds to one plotted value, the row number is shown in the correct plot color (on colorscreens).

Update PlotsThe command is enabled while a simulation database is running.

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Figure V/23: part of the Plot Window before (left) and after (right) a Update Plotscommand.

This commands updates all plots so that the most recent computation results are shown.While a simulation is running the graphics in the Plot Window sometimes do not reflect theactual integration status. Nodus tries to decrease the memory use of plots by converting thestream of points generated by the integration routines into continuous lines whenever possible.This is accomplished by buffering a small amount of points before plotting them.When several plots are drawn they may seem to stop at different time values. This does notmatter if no one is watching the screen during the simulation. To check the results intermittentlyduring a simulation select Update Plot to flush the plot buffer so that all plots show exactlyhow far the simulation has progressed.

Text Output…The command is always enabled if a simulation database exists. It is italicized if Text Outputis active, once the simulation has started running. A check mark shows whether it is active.

Figure V/24: the Text Output dialog window.

This command controls the output of simulation results to a text output file on disk.The output settings can be selected either before a simulation has started or while it is running,but once Nodus has started to output the settings cannot be edited anymore.If text output is active then at the next Run command a minifinder window prompts for the textfilename (default is the simulation name + ‘ results’). The text file is always saved to the samefolder (or the desktop level) as the Nodus applications, the user can specify another folder but itwill have no effect.

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Delete all: clears all Text output settings in the simulation database. Text output is nolonger active.

Output options:One of two output modes can be selected in the upper left part of the dialog.

Output at major time step: at the end of each major time step (see IntegrationSettings) the results are written to the file. Output consists of the time in ms, followed bythe selected simulation parameters. Warning: this option can create huge disk files.Output maxima/minima: can be used to time action potentials, EPSPs, IPSPs, etc. Noduschecks at the end of each minor integration step if the selected simulation parameter hasreached a maximum or minimum and outputs the parameter array index, the time of occur-rence (in ms) and the value of the parameter if this is the case. Either maxima or minima aloneor both together can be monitored. Because of the additional calculations involved, thiscommand decreases the integration speed slightly.Threshold: this text box appears only for the Output maxima/minima option. Inte-gration methods may oscillate slightly (see ‘Integration Methods’ in chapter III), producingmany “fake” maxima and minima. To restrict output to the “real” maxima and minima athreshold value has to be specified; this specifies how much the parameter value has to changein % versus the tentative maximum/minimum before it is considered a real maximum/mini-mum. Usually large threshold values (like 50 to 100%) will give satisfactory results, but onemay have to experiment.Trigger all output on value #1: this check button appears only for the Outputmaxima/minima option. If it is checked only the first selected simulation parameter will bemonitored for maxima/minima. All following simulation parameters will be send to outputtogether with the first one whenever the first one reaches a maximum or minimum.

Format optionsThe upper right part of the dialog contains some options which affects all output.

Separated by TABs: if this option is checked all simulation parameters will be written asfloating point output separated by tabs, otherwise they are separated by spaces. This option isuseful if one wants to import the text file into a spreadsheet, graphics application or a statisticspackage.Make legend: a legend describing for each array index the selected simulation parameters isput at the start of the text file. Not available in Nodus 3.1.2.

Output data:The lower part of the dialog contains the simulation parameters selected for output. The dialogcan show selected simulation parameters for only one value type at once, but several value typescan be send to output. The different output value types are stored consecutively and one can goback and forth between them.

Previous value type: one can use this button to look at other value type selections whenmore than one value type has been selected for output. The button is dimmed if one is lookingat the first output selection (row numbers start with one). Pressing this button stores allchanges made to output of the presently shown value type in memory.Next value type: use this button to select or view additional output of another value type.One can also use this button to enter more than 5 simulation parameters of the same valuetype. It will be dimmed if no simulation parameters are selected in the dialog for the presentvalue type. Pressing this button stores all changes made to output of the presently shownvalue type in memory.

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Value type: Select the output value type with the value type popup menu (Fig. V/18). Ifsome simulation parameters have already been selected for this value type, they will beshown. Changing the value type stores all changes made to output of the presently shownvalue type in memory. The value Unit (which is specific for the selected value type) can bechanged with the unit popup menu.Output all values of this type for selected neuron: instead of one (selected) simu-lation parameter, all simulation parameters of the same value type present in the same neuronmodel will be send to output. Only available for Output at major time step. Beware ofthe huge disk files!

Below the value type data are 5 parameter selection popup rows (see ‘Selecting SimulationParameters’ in chapter IV) which are used to specify which simulation parameters will be writtento output. Each row corresponds to one simulation parameter selection. The row number is thenumber of the selected parameter in the list of all output selections (it can be any numberbetween 1 and 20). Row numbers are empty till an output selection is confirmed.

Current Clamp…The command is always enabled if there is a simulation database in memory. It is italicizedwhen the simulation has finished running. A check mark shows whether it is active.

Figure V/25: the Current Clamp dialog window. Right: alternate views for white noisecurrents (upper right) or non-cyclical currents (bottom right).

With this command current injections in one or several compartments of the neuron model canbe specified or changed. Multiple current injections in the same compartment are possible, ifthey occur simultaneously they will be added together. Use this feature to make complex currentcombinations.The Current Clamp settings can be edited while a simulation is running. This causes noproblems, retroactive changes are of course not possible.

Delete all currents: clears all Current Clamp settings in the simulation database.Current Clamp is no longer active.

Compartment selection:The box in the upper part of the dialog window contains a parameter selection popup row (see‘Selecting Simulation Parameters’ chapter IV) of the membrane potential type (3 popup menus).These allow selection of a compartment to inject currents into, any changes made to the presentlyshown current injection will be stored into memory. If no Current Clamps are defined thefirst compartment in the simulation database will be shown, otherwise the first compartmentwith current injections.

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Delete currents in this compartment: all current injections for the compartment shownare cleared. Current injections in other compartments are not affected.Show all compartments: the compartment selection popup menu shows all compartmentsin the selected neuron (default). If this option is not checked, the popup menu will onlycontain compartments for which current injections have been defined; this is a quick way toidentify such compartments in large models.

The lower part of the compartment box has a graphic display of all the current injectionsdefined in this compartment. The time axis of the current display is the total simulation time asdefined in the Integration Settings, the vertical axis is scaled to show the maximum current.Individual current injections are shown light blue, the sum of all currents in the selectedcompartment dark blue, and zero current is shown as a broken red line. The current display isnot updated continuously, click on the display to update it. The interpolation accuracy of thedisplay (and the time needed to update it) depend on the major time step (see IntegrationSettings).Current selection:Several different currents can be injected into a compartment simultaneously or consecutively.Currents that are injected simultaneously will be added together.Only one current injection can be viewed and edited at once. Different current definitions in theinjection list in one compartment are numbered consecutively.

Previous: shows the preceding current definition in the injection list for this compartment,dimmed if current # 1 shown. Pressing this button stores all changes made to the presentlyshown current injection in memory.Next: shows the next current definition in the injection list for this compartment, or an emptycurrent definition if no next injection is defined. Pressing this button stores all changes madeto the presently shown current injection in memory.Duplicate: duplicates the presently shown current definition, puts it at the end of the injectionlist and displays it. Dimmed if the current definition is empty.Delete: deletes the presently shown current definition and shows the next one in the injectionlist for this compartment. Dimmed if the current definition is empty.

The rest of the buttons and text boxes define the presently shown current definition.The icons at the upper left determine the type of current. They are (from left to right): steadycurrent, ramp current, triangular current, sinusoidal current and white noise current. A “singleunit” of current definition corresponds exactly to what is shown in the icon.

Begin: time in ms at which the current injection will start. This time has to be smaller than themaximum simulation time (see Integration Settings) and has to be a multiple of the majortime step (idem).End: time in ms at which the current injection will end. This time has to be smaller than themaximum simulation time (see Integration Settings) and has to be a multiple of the majortime step (idem).Amplitude: (maximum) amplitude of the current in nano-amperes (nA). If no value isspecified, the current definition is considered empty. Corresponds to the amplitude of steadycurrents and the maximum for range and triangular currents. Sinusoidal and white noisecurrents fluctuate between plus and minus the amplitude.Cyclical: if this option is not selected the defined current “unit” will be active during thesimulation from the Begin till the End time. If cyclical is selected, the current definition“unit” will be repeated several times in between the Begin and End time, the frequency ofrepetition depends on the specified Period. Default is off.

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Period: the period in ms for a Cyclical current definition. The current definition “unit” willhave a duration of one period and will then be repeated till the End time is reached. Thisvalue is not shown when Cyclical is not selected. The period has to be smaller than themaximal simulation time (see Integration Settings) and it has to be a multiple of the majortime step (idem). Default is from Begin to End (one period).On from…to…: the part of the Period in ms during which the current injection is active.The main use of this feature is to create repetitive current pulses with a cyclical steady current;the Period determines its frequency, the “on from…to” values determine the length of thepulse. They can also be used with non-steady currents to produce even more complex currentinjections. These values are not shown when Cyclical is not selected. The “on from…to”values have to be smaller than the Period and they have to be a multiple of the major timestep (see Integration Settings). Default is the full Period.Seed: with white noise currents a seed number for the random number generator has to bespecified. This allows the user to repeat simulations with the same series of pseudo-randomcurrent injections. White noise current is simulated with pseudo-random numbers generatedby a Monte Carlo method. White noise currents cannot be cyclical.

Voltage Clamp…The command is always enabled if there is a simulation database in memory. It is italicizedwhen the simulation has finished running. A check mark shows whether it is active.

Figure V/24: the Voltage Clamp dialog window. Right: voltage clamp of a secondneuron.

Complex voltage clamps can be simulated, the user only has to specify the length and potentialsteps for up to 5 clamping periods in one neuron. In each period the potential can be stepped todifferent values, however if steps (different from 0 mV) are specified for different periods allthese periods will step together. Steps are allowed in any period, so there is a free choice of thestepping period within the voltage clamp cycle. Voltage clamps start from the beginning of thesimulation and continue for the complete voltage clamp length, i.e. the length of a voltage clampcycle (the sum of the lengths of all periods) multiplied by the maximum number of steps.Another neuron in the network can be clamped to a continuous, steady potential.To get standard voltage clamp graphics one should select 2 plots (Configure Plots) withrepetitive sweeps, time axis from zero to the length of a voltage clamp cycle, and plot themembrane voltage on the first and the clamping current on the second axis. An example isshown in the ‘Test-cell 1 clamp’ simulation data file.The Voltage Clamp settings can be edited while a simulation is running, but this can causeunexpected effects. Be careful!

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Delete voltage clamps: clears all Voltage Clamp settings in the simulation database.Voltage Clamp is no longer active.

Neuron #1 and compartment selection:The box in the upper part of the dialog window contains either a neuron selection popup menuor a parameter selection popup row (see ‘Selecting Simulation Parameters’ chapter IV) of themembrane potential type (3 popup menus). If the neuron popup menu shows ‘not used’voltage clamps are disabled.

All compartments: if this button is selected all compartments in the selected neuron will beclamped to the selected voltages. Only a neuron selection popup menu will be shown in thespace above.Compart: if this button is selected only the selected compartment of the selected neuron willbe clamped to the selected voltages. Next to this button a compartment preselection popupmenu and a compartment selection popup menu allow selection of the clamped compartment.

It is up to the user to decide which alternative gives the best approximation of the actual bio-logical experiment. For small, compact cells it might be possible to clamp the complete cell, butin a lot of cases only the soma and the large branches close to the soma are effectively clamped.If one compartment is clamped, then the compartments which are not clamped will still affect theclamping current by passive flow of current to the connected compartments.Periods:For the first neuron 5 clamping periods can be specified, they are executed in the order shown.A clamping period is active if its length is longer than zero. During the first cycle the clampedcompartment(s) are set to the from voltage; during the following cycles the clamp voltage isincreased by the step value, till the to voltage is reached. The voltage clamp experiment is fin-ished after the cycle during which the period with the largest number of steps has reached its tovoltage.Nodus checks if the total simulation time (Computation Settings…) matches the voltageclamp specification, if not then an alert message is shown:Neuron #2 and compartment selection:If neuron #1 is voltage clamped, a steady clamping voltage can be specified for a secondneuron. If the neuron popup menu shows ‘not used’ no second voltage clamp is defined.

All compartments: if this button is selected all compartments in the selected neuron #2 willbe clamped to the selected voltage. Only a neuron selection popup menu will be shown in thespace above.Compart: if this button is selected only the selected compartment of the selected neuron #2will be clamped to the selected voltage. Next to this button a compartment preselection popupmenu and a compartment selection popup menu allow selection of the clamped compartment.clamp at: the second neuron will be clamped at this voltage during the complete voltageclamp experiment.

Warning messagesWhen voltage clamp settings have been changed and the user presses OK, the total voltageclamp experiment length is computed and compared with the simulation length. If simulationlength is too short a warning is shown:

Figure V/25: the warning when simulation length does not match voltage clamp length.

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No Change: simulation length remains the same, change it manually with IntegrationSettings.Change Length: the simulation length is reset to the voltage clamp experiment time.

Nodus checks whether the settings for the simulation plot (Configure Plots) match thevoltage clamp specification, if not then a warning is shown.

Figure V/26: the warning when plot settings do not match the voltage clamp settings.

No Change: the plot settings remain as they were, change them manually with ConfigurePlots.Change Plot: the end of all time axes is reset to the length of one clamping cycle, repetitivesweeps is selected and the membrane potential in the first clamped compartment is plotted onthe first axis and the clamping current on the second axis. If a second neuron is clamped, athird axis is added to show its clamping current.

Synaptic Firing Times…The command is always enabled if there is a simulation database containing type 1 synapses inmemory. It is italicized when the simulation has finished running. A check mark showswhether it is active.

Figure V/27: the Synaptic Firing Times dialog window.

This command can be used to specify times at which type 1 synapses will discharge during thesimulation in single neuron models or independent of any firing caused by presynaptic neuronalactivity in network models. See the ‘Simulation of Synapses and Connections’ section inchapter III for a definition of a type 1 synapseThe Synaptic Firing Times settings can be edited while a simulation is running. This causesno problems, retroactive changes are of course not possible.

Delete all firing times: clears all Synaptic Firing Times settings in the simulationdatabase. Synaptic Firing Times is no longer active.

Compartment selection:The box in the upper part of the dialog window contains a parameter selection popup row (see‘Selecting Simulation Parameters’ chapter IV). These allow selection of a compartment and atype 1 synapse to set firing times form, any changes made to the presently shown firing timeswill be stored into memory. If no Synaptic Firing Times are defined the first compartmentwith a synapse in the simulation database will be shown, otherwise the first compartment withpreset firing times.

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Fire single synapses: only the selected synapse will fire (default).Fire all synapses in neuron at once: all the type 1 synapses in all the selected neuronswill fire at the preset firing times. This option works on all the preset firing times. Theparameter selection popup row will still show compartments and synapses, but only theneuron selection is relevant.Delete all firing times for this synapse: all current injections for the compartmentshown are cleared. Current injections in other compartments are not affected.Transmitter amount: (fixed) amount of transmitter that will be released at the preset firingtimes shown for this synapse. Different transmitter amounts may be specified for differentsynapses (default is 1.0, arbitrary units).

Firing times:For each selected synapse (or neuron) 12 firing times may be specified. They should be enteredin consecutive order, from left to right and up to down. Any value entered corresponds to asynaptic firing time (also 0.0 ms). The firing times do not have to be multiples of the major timestep (see Integration Settings).

Block Ionic Currents…The command is always enabled if there is a simulation database containing voltage dependentconductances in memory. It is italicized when the simulation has finished running. A checkmark shows whether it is active.

Figure V/28: the Block Ionic Currents dialog window. At the right: an example of thespecial conductance popup menu.

This command can be used to selectively block ionic conductances, simulating the bath appli-cation of antagonists. Conductances may be blocked completely (100%) or partially (more than0 %). Several currents may be blocked, but they all have to be blocked during the same periodin the simulation.

Delete all blocks: clears all blocking factors in the simulation database. Block IonicCurrents is no longer active.Begin: start of the current block period in milliseconds (ms). Should be a be multiple of themajor time step (see Integration Settings).End: end of the current block period in milliseconds (ms). Should be a be multiple of themajor time step (see Integration Settings).Block: a selection conductance selection popup menu allows the selection of a conductancefor which then a blocking factor between 0 and 100% can be specified. The popup menu alsoshows for each conductance its blocking factor (see Fig. V/28).by: a text box allows entering or editing of the blocking factor for the conductance selectedwith the conductance selection popup menu. The blocking factor is stored in memory whenanother Block conductance is selected with the popup menu or OK is pressed.

Measure WindowThe command is enabled if the Plot Window is active. A check mark shows whether a MeasureWindow is open.

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This commands opens a Measure Window or selects it and activates measuring (Fig. V/29).

Figure V/29: an example of measuring.

See the description of the Measure Window at the begin of this section for a description ofmeasuring.

Plot WindowThe command is always enabled if there is a simulation database in memory. A check markshows whether the Plot Window is shown.This commands shows the Plot Window or selects it.See the description of the Plot Window at the begin of this section.

Status WindowThe command is always enabled if there is a simulation database in memory. A check markshows whether a Status Window is open.This commands opens a Status Window or selects it and updates the simulation status.See the description of the Status Window at the begin of this section for a description of itscontents.

Time WindowThe command is always enabled if there is a simulation database in memory. A check markshows whether the Time Window is shown.This commands shows the Time Window or selects it.See the description of the Time Window at the begin of this section.

Network MenuThe network menu is file specific, its commands are enabled only when a network definitionwindow is active (i.e. is in the front) and the commands will operate on the network definitionfile connected to that window. Only one network definition file can be stored in memory. TheNew, Open, Close and Save Network commands (File menu) operate on this file.

WindowsNetwork definition windowA network definition window displays which neurons are part of the network. The name of thecorresponding file is the title of the window and is listed at the bottom of the Network menu.A check mark before that menu item shows whether the window is shown, the command can beused to select or open the window.

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Fig. V/30: a network definition window.

The network definition window (Fig. IV/14, V/30) lists all neurons in the network. Eachneuron has a local name, and a link to a neuron definition file; lines showing None mean thatthere is no neuron. Only 8 neurons of up to 40 are shown at a time, they are numberedconsecutively (left side of the window).

Previous: show the preceding set of 8 neurons, any changes made to the presently shownset are stored in memory.Next: show the next set of 8 neurons, any changes made to the presently shown set arestored in memory.

Besides the Next and Previous buttons is space for a Comment describing the network.Local name: the name of the neuron in the network, specific for this network definition.This name will be shown in the neuron popup menu during selection of simulation para-meters. If you do not specify a local name, Nodus will generate one if a link to a neurondefinition file is set.Neuron definition: a neuron popup menu, listing all neuron definitions in memory, allowsselection of a neuron definition file that is linked to the local name of the network neuron. Thesame neuron definition file can be used for several network neurons (as in Fig. V/30), or thenetwork can consists of different types of neurons (specified by separate neuron definitionfiles). Note that all the neuron definitions linked to a network have to be in memory, or thepopup menus will not work properly (see the Open all linked files in the Open Networkdialog window). Set to None to remove a neuron from the network.Connections: shows the number of presynaptic contacts (Out) and postsynaptic (In)connections present between this neuron and other neurons in the network. Connectionscannot be changed in the network definition window, use the Synaptic Connections menucommand.

Menu CommandsThe Network menu commands are active when a network definition window is the highlightedwindow. At the bottom of the menu all network definition files in memory are listed.

Fig. V/31: the Network menu.

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Synaptic Connections…With this command one can specify or edit synaptic connections between the neurons of anetwork. It is enabled when 2 or more network neurons have been defined. The connection isspecified at the presynaptic neuron, each network neuron can be presynaptic to up to 8 otherneurons. In Nodus 3.1 all connections are “hard-wired”, there is no synaptic plasticityimplemented. Synaptic connections can have delays, this is a simple but effective way to simu-late (long) axons.

Fig. V/32: the Synaptic Connections dialog window.

Connections from: a neuron popup menu allows the selection of any network neuron asthe presynaptic neuron, any changes made to the presently shown connections are stored inmemory. The dialog window shows all the connections that have been defined from theselected neuron.

ConnectionsConnections are defined by linking a presynaptic compartment (having a transmitter release site)with a specific synapse at a postsynaptic compartment of another neuron. This is done withpopup menus.

Compartment: a compartment popup menu allows the selection of the presynaptic compart-ment. As default the first compartment having a transmitter release site in the selected pre-synaptic neuron will be shown.Neuron: a neuron popup menu allows the selection of the postsynaptic neuron. None isshown for undefined connections (in Fig. V/32 only one connection has been defined, initiallyall neuron popup menus will be None). One cannot connect a neuron to itself.Compartment: a compartment popup menu allows the selection of the postsynaptic compart-ment. No popup menu is shown if the postsynaptic neuron is set to None. The compartmentpopup menu will only show compartments with synaptic currents. As default the first suchcompartment in the postsynaptic neuron is shown.Synapse: a subdefinition popup menu allows the selection of any of the synaptic current sub-definitions tied to the postsynaptic compartment (up to 3). As default the first synaptic currentis shown. Nodus 3.1 does not prevent you form linking transmitter release sites to inappro-priate postsynaptic sites (see the ‘Simulation of Synapses and Connections’ section in chapterIII).Delay, ms: the delay between the presynaptic transmitter release and the corresponding post-synaptic conductance in milliseconds (ms). Default is 0 ms, use higher values to simulateaxons (as a transmission line delay).

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Neuron MenuThe neuron menu is file specific, its commands are enabled only when a neuron definitionwindow is active (i.e. is in the front) and the commands will operate on the neuron definition fileconnected to that window. Several neuron definition files can be stored in memory at once. TheNew, Open, Close and Save Neuron commands (File menu) operate on the file connectedto the active window.

WindowsNeuron definition windowA neuron definition window displays general data about the neuron. The name of the corre-sponding file is the title of the window and is listed at the bottom of the Neuron menu. Acheck mark before that menu item shows whether the window is shown, the command can beused to select or open the window.

Fig. V/33: a neuron definition window.

The neuron definition window (Fig. IV/12, V/33) consists of 2 parts, the upper part contains thedescription and controls for the neuron, the lower part its specific cable parameters.Neuron description:

Created on: the day on which the corresponding file was created (with a Save Ascommand).Modified: the day on which the corresponding file was last Saved.

Under the creation date is space for a Comment describing the neuron.Number of compartments: the total number of compartments in this neuron definition.Default setting is 100. This number can be changed at any time. Note however that newcompartments are not allocated automatically; their sizes and connections have to be definedby the user. If the number of compartments is reduced, Nodus prompts for confirmationbefore deleting defined compartments.Tree model format: if this option is selected (default) the compartments have to be definedin a centrifugal way, starting from the soma. Nodus checks if the connections follow treeformat rules and connects branches automatically back to the parent compartment. See the‘Nodus Implementation of Compartmental Models’ section in chapter III for an accuratedescription of the rules enforced by the tree format. Do not switch off this option unless it isnecessary to create an unorthodox model. The tree model format enforces good modelingpractices, it also prevents stupid mistakes (which can happen if several 100 compartmentshave to be defined!). The Tree model format button is on and disabled if the 3-dimcoordinates option is on.

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3-dim coordinates: if this option is selected (default is off) the compartment definitionwindow (Fig. V/35) will show the three dimensional coordinates of the center of the proximaland distal faces of the compartment cylinders. The 3-dim coordinates option can only beused if the Tree model format option is on, because of the way the coordinates are storedin memory. When it is selected the Tree model format button will be disabled. Using 3-dim coordinates has no advantages in Nodus 3.1, it reduces the maximum number ofcompartments from 4000 to 3000. Future Nodus versions may have 3-dim drawings, whichwill need these coordinates.

Cable parametersThe significance of the cable parameters is explained in the ‘The Mathematics of CompartmentalModeling’ and ‘Passive Membrane Models’ sections in chapter III.

Membrane capacitance: membrane capacitance of the equivalent cable in microfarads persquare centimeter (µF/cm2). This value is used for all compartments.Membrane resistance: membrane resistance of the equivalent cable in kilo-ohm-square-centimeters (kΩcm2). This value may vary between compartments:

Constant: the value entered for membrane resistance is used in all passive compartments.Variable: membrane resistance can be defined for each passive compartment separately inthe compartment definition window (Fig. V/35), the value entered in the neuron definitionwindow is used as default.From compartment # to: this option makes the use of 2 different values for membraneresistance easy. All passive compartments within this range of compartment ‘From’ tocompartment ‘to’ have for membrane resistance the value specified at the end of the rangespecification. The first value specified above is used for the other compartments. In mostcases the first value will correspond to the membrane resistance for the soma, the secondvalue is used for the dendrites (these can be defined as a continuous range in a tree modelformat).

Cytoplasmic resistance: cytoplasmic resistance of the equivalent cable in ohm-centimeters(Ωcm). Remark that kΩ is used for membrane resistance and Ω for cytoplasmic resistance!This value is constant for all compartments.Resting membrane potential: resting membrane potential in millivolts (mV) for allpassive compartments of the neuron model. The Nodus integration routines perform better ifa non-zero value is used; enter a biologically realistic value.Scaling factor: allows the global scaling of all compartment membrane surfaces, withoutaffecting the cytoplasmic resistances between them. See the ‘The Mathematics of Compart-mental Modeling’ section (Eq. 9) in chapter III for more details. The default value is 1.0 (noscaling).

Compartment definition windowA compartment definition window displays specific data about one compartment. The name ofthe corresponding neuron definition file, followed by the compartment number, is the title of thewindow. The name of the file is listed at the bottom of the Neuron menu. A check markbefore that menu item shows whether the window is shown, the command can be used to selector open the window.The compartment definition window (Fig. IV/13, V/34, V/36) consists of several parts. Theupper part contains the description and morphology of the compartment, below that two lines oftext show the compartment cable parameters. A large box at the lower left side shows theconnections to other compartments. At the lower right ties to subdefinitions and (if present) thethree dimensional coordinates of the compartment are shown.

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Fig. V/34: a compartment definition window of a constant membrane resistance neuronmodel without 3-dim coordinates.

Compartment description and morphology:Name: name of the compartment. Any word with up to 10 characters can be used to giveeach compartment a specific name which will be shown in compartment popup menus duringselection of simulation parameters. You do not have to give names; by reserving Names for“special” compartments one can shorten the compartment popup menu if the Show onlynamed comparts option in the Preferences (Edit menu) is on (see the ‘SelectingSimulation Parameters’ section in chapter IV). Automatic naming is available in the ImportNeuron command.Structure type: the structure type popup menu (Fig. V/35) contains 14 predefined structurenames that describe different parts of neurons. Use this menu to distinguish between differentsegments of the neuron. The structure type name will be used in compartment popupmenus if no compartment Name is specified (see the ‘Selecting Simulation Parameters’section in chapter IV).The structure type has no effect on the compartment shape. Defaultis soma for the first compartment and undefined for all the following ones.Sphere / Cylinder: select between these 2 options to determine the compartment shape.The compartment shape determines how the membrane surface and cytoplasmic resistance arecalculated. Sometimes the Sphere option may be disabled if the tree model option(neuron definition window) is on, because a spherical compartment would be illegal (see the‘Nodus Implementation of Compartmental Models’ section in chapter III).Diameter: diameter in micrometer (µm) of the compartment. Has to be larger than zero!Length: length in micrometer (µm) of the compartment. Has to be larger than zero! TheLength text box is not shown if the compartment is spherical.

Fig. V/35: the structure type popup menu.

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Compartment cable parameters:The compartment cable parameters are calculated fields (except for Membrane resistance).These values may change if the sizes or shape of the compartment are altered or if some links areadded or removed (see the ‘The Mathematics of Compartmental Modeling’ section in chapterIII). The displayed values are updated every time you select another text box or button.

Membrane capacitance: the membrane capacitance in picofarad (pF) for the compartment(Fig. V/34). Will only be shown for neuron definitions with a constant value membraneresistance (or 2 values, neuron definition window).Membrane resistance: shows the value for specific membrane resistance in kilo-ohm-centimeter-squared (kΩcm2) that is used to calculate the actual membrane resistance of thecompartment, shown in kilo-ohm (kΩ, Fig V/36). The default value for membrane resistanceis used until the user has edited the value. Will only be shown for neuron definitions with avariable membrane resistance (neuron definition window).Electrotonic length: the electrotonic length for the compartment.Time constant: the time constant in milliseconds (ms) for a passive compartment. The timeconstant showed for excitable compartments (tied to a ionic or synaptic currents sub-definitions) is not relevant.

Fig. V/36: a compartment definition window of a variable membrane resistance neuronmodel and with 3-dim coordinates.

Connections to other compartmentsThe lower left part of the dialog window contains a list of all the connections or Links to othercompartments. For each new connection the compartment to link to, a type of connection (nodeor branch) and a weight factor (between 1 and 100) is defined. For most compartments this listwill start with one or several dimmed connections: the connection to the parent compartment orcross connections (the connections to other proximal compartments for node connections).These connections cannot be changed in this compartment definition, they can only be changedat the definition of the parent compartment, i.e. the more proximal compartment which is(usually) shown as first in the list.The first compartment in each neuron definition can now have up to 24 Links to other compart-ments, all other compartments have a maximum of 6 links. In the first compartment two buttonscontrol the range of connections displayed, the numbers at the left show the range.

>>: moves forward through the connection range (with jumps of 6).<<: moves back through the connection range (with jumps of 6).Link to: the compartment number of the compartment to which this one is connected. If it isproximal relative to the compartment shown, the text box is not editable; distal connectionscan be edited.

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The type of connection is selected with an icon (Fig. V/37). Push the desired connection typeand the icon will be highlighted (default is a node connection). Dimmed icons refer to connec-tions that have been defined elsewhere (at the parent compartment) and cannot be changed in thiscompartment definition. If the tree model option is off (neuron definition window) or forspherical compartments all branch connection icons will be dimmed.

Fig. V/37: the connection type icons. At the left a node connection icon, at the right abranch connection icon.

Node connection: the end of one cylinder is connected to the end of the next cylinder, etc.Each compartment is cross-connected to all other compartments connected by the same node.Branch connection: compartment is connected to the center of the cylindrical compartmentof the parent branch. Different branch connections to the same parent compartment are notlinked to each other.Weight factor: for weight factors (n) larger than 1, Nodus computes as if n identicalcompartments are connected to this parent compartment.

See the ‘Nodus implementation of compartmental models’ section in chapter III for a completedefinition of node and branch connections and of weight factors.SubdefinitionsThe lower right part of the compartment definition window contains several subdefinition popupmenus that can tie subdefinitions to the compartment. See the ‘Nodus Files’ section in chapterIV for more details on subdefinitions in neuron definition files.

Ion currents: a popup menu listing all ionic currents subdefinitions allows you to addvoltage dependent conductances to the compartment, making it excitable. If None is shownthe compartment has passive membrane.Synapse #1,2,3: a popup menu listing all synaptic currents subdefinitions allows you toadd up to 3 (different) postsynaptic sites to the compartment. No postsynaptic sites arepresent if all popup menus show None.Transmitter: a popup menu listing all transmitter release subdefinitions allows you to add apresynaptic site to the compartment. There is no transmitter release if the popup menu showsNone .

Three dimensional coordinates:If the 3-dim coordinates option in the neuron definition window is on, the coordinates willbe shown in the lower right corner of the compartment definition window (Fig. V/36).

x0, y0, z0: the 3-dim coordinates of the center of the proximal face of the compartmentcylinder (or the center coordinates of a spherical compartment). These cannot be edited, theyare determined by the coordinates of the parent compartment.x1, y1, z1: the 3-dim coordinates of the center of the distal face of the compartment cylinder(or the center coordinates of a spherical compartment). These can be edited, but be sure thatthe values fit to the compartment Length! Note also that the 3-dim coordinates are notadapted when the compartment sizes are changed manually. They are adapted when theOptimize Model, Fuse Compartments or Split Compartment commands are used,but not when the Scale Sizes command is used.

Menu CommandsThe Neuron menu commands are active when a neuron definition window or compartmentdefinition window is the highlighted window. At the bottom of the menu all neuron definitionfiles in memory are listed.

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Fig. V/38: the Neuron menu.

ParametersThe command is enabled if the front window is a compartment definition window. It removesthe present compartment definition window and shows the neuron definition window of thesame neuron definition file.

Go to Compartment…The command is enabled if the front window is a neuron or compartment definition window.Allows the user to display any compartment from the neuron definition file.

Fig. V/39: the Go to Compartment dialog window.

A dialog window prompts for a compartment number (Fig. V/39). After pressing OK thepresent neuron or compartment definition window is removed and the compartment definitionwindow of the requested compartment number is shown. If the shift key is pressed when thiscommand is invoked no dialog window will be shown; type the desired compartment numberand hit the return key (this is slightly faster).

Previous CompartmentThe command is enabled if the front window is a compartment definition window with compart-ment number larger than one. The present compartment definition window is removed and thecompartment definition window of the preceding compartment (by compartment number) isdisplayed.

Next CompartmentThe command is enabled if the front window is a neuron or compartment definition window.The present neuron or compartment definition window is removed and the compartment defi-nition window of the next compartment (by compartment number) is displayed.

Fuse Compartments…The command is enabled if a compartment definition window is active. It can be used to fusetwo or more cylindrical compartments linked by node connections together. The total number ofcompartments will be decremented, other compartments will be renumbered and all branchesfrom the original compartments will be connected to the fused compartment.

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Fig. V/40: the Fuse Compartments dialog window.

The lengths, diameters and 3-dim coordinates of the fused compartment will be computedautomatically from the compartment sizes (Eq. 14-17, chapter III). The fused compartment willinherit as many ties to subdefinitions from the original compartments as possible. It will get thename of the first compartment in the range.Use this command to fuse small compartments with the same or similar diameters. Fusingcompartments with quite different diameters or fusing many compartments will reduce theelectrotonic accuracy of the compartmental model.In the upper part of the dialog window (Fig. V/40) the name of the neuron definition file and thecompartment number of the first compartment to fuse is shown.

Fuse with compartments…to…: determines how many compartments will be fused withthe one shown at the top. All compartments in between the first (#6 in Fig. V/40) and the last(#7 in Fig. V/40), that are linked by first node connection in the list of connections, will befused (inclusive the first and the last). The range to fuse should be a continuous, unbranchedsequence of compartments, otherwise Nodus will complain.Convert node connections to branches: if this option is selected (default) all side-branches linked by node connections to compartments between the first and the last of therange to fuse will be converted to branch connections. Because branch connections are linkedto the center of the (fused) parent compartment this will slightly improve electrotonicaccuracy. If the option is not selected such side-branches will be linked by nodes at the distalside of the fused compartment. This button will be dimmed if no side-branches are present.WARNING: there might not be enough space to connect all side-branches to a fusedcompartment (maximum of 6 connections). Nodus checks whether there is enough spacebefore changing the connections, but it might run into problems if a lot of compartments arefused and scramble the connections. Better be save than sorry: Save the original file first!

Split Compartment…The command is enabled if a compartment definition window is active. It splits a cylindricalcompartment in two or more compartments with consecutive compartment numbers and linkedby node connections. The total number of compartments will be increased and other compart-ments may be renumbered.

Fig. V/41: the Split Compartment dialog window.

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The lengths and 3-dim coordinates of the split compartment will be computed automatically; allnew compartments will have the same length. Ties to ionic currents and synaptic current sub-definitions will be spread over all new compartments if the maximum conductance is determinedby compartment surface (i.e. is in mS/cm2), otherwise only the first compartment will keep theties. Ties to transmitter release sites are always distributed over all the new compartments.They will be renamed automatically.Use this command to split long compartments, it will increase the electrotonic accuracy of thecompartmental model (but reduce integration speed).In the upper part of the dialog window (Fig. V/41) the name of the neuron definition file and thecompartment number of the compartment to split is shown.

Split into…compartments: the number of compartments that will replace the originalcompartment. Default is 2, may be any larger number. Remember however that very shortcompartments may reduce integration speed enormously!Distribute proximal node connections: some of the side-branches connected by nodeconnections to the proximal part of the original compartment will be reconnected to more distalparts of the split compartment. This distributes the side-branches uniformly over the length ofthe original compartment. The effect upon electrotonic accuracy depends on the morphologyof the real neuron. This button will be dimmed if no proximal side-branches are present.Distribute distal node connections: some of the side-branches connected by nodeconnections to the distal part of the original compartment will be reconnected to more proximalparts of the split compartment. This distributes the side-branches uniformly over the length ofthe original compartment. The effect upon electrotonic accuracy depends on the morphologyof the real neuron. This button will be dimmed if no distal side-branches are present.Convert branch connections to nodes: if this option is selected (default) all side-branches linked by branch connections to the original compartment will be converted to nodeconnections. This will usually improve electrotonic accuracy. If the option is not selected thebranch connections will be distributed over new compartment. This button will be dimmed ifno branch connections are present.WARNING: Nodus checks whether there is enough space before changing connections, butit might run into problems in very complex neuron models and scramble the connections.Better be save than sorry: Save the original file first!

Optimize Model…The command is enabled if the front window is a neuron or compartment definition window. Itoptimizes the neuron for fast, but accurate integration by fusing or splitting compartments to getcompartments with good electrotonic lengths. The total number of compartments may increaseor decrease and a lot of compartments will be renumbered.

Fig. V/42: the Optimize Model dialog window.

The lengths, diameters and 3-dim coordinates of the fused or split compartments will becomputed automatically from the compartment sizes (Eq. 14-17, chapter III).

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This routine calls the Fuse Compartments and Split Compartment routines to do theactual work. See the information about these commands for more details. Use this commandafter a new neuron model has been made, after making large changes to the cable parameters orafter a Scale Sizes command. Optimizing the model should improve both the electrotonicaccuracy of the compartmental model and the integration speed. You can control how much theoriginal morphology may be changed. This command can take a long time to execute if there area lot of compartments!In the upper part of the dialog window (Fig. V/40) the name of the neuron definition file isshown.

Electrotonic lengths from…to…: determines the range of optimal electrotonic lengthsfor all compartments that Nodus will try to achieve. The suggested values are a goodcompromise between electrotonic accuracy and integration speed.Fuse compartments if needed: if this option is selected (default), Nodus will fuse shortcompartments if possible. If it is not selected, electrotonic lengths optimal for fast integrationmay not be achieved, but the original morphology will be conserved better.Split compartments if needed: if this option is selected (default), Nodus will split longcompartments. If it is not selected, electrotonic lengths optimal for accuracy may not beachieved. One should always select this option.Redistribute node connections: if this option is selected some side-branches connectedby node connections to a compartment that is split will be reconnected to different parts of thesplit compartment, to distribute them uniformly over the length of the original compartment.The original morphology will be conserved better if this option is not selected. Branchconnections to a split compartment are always converted to node connections.Branch connections may be used: if this option is selected side-branches linked by nodeconnections to compartments that are fused will be converted to branch connections. Theoriginal morphology will be conserved better if this option is selected, but the electrotonicaccuracy may be diminished.WARNING: there might not be enough space to connect all side-branches to a fusedcompartment (maximum of 6 connections) or for new compartments. Nodus checks whetherthere is enough space before changing the connections, but it might run into problems if a lotof compartments are fused and scramble the connections. This command is more “danger-ous” than individual Fuse Compartments or Split Compartment commands. Better besave than sorry: Save the original file first!

Scale Sizes…The command is enabled if the front window is a neuron or compartment definition window. Itirreversibly scales all compartment sizes, without changing the number of compartments or theirconnections.

Fig. V/43: the Scale Sizes dialog window.

Use this command to compensate for known global measuring errors in a neuron, for exampleshrinkage or to change compartment sizes in homogeneous models (e.g. an axon) fast.

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In the upper part of the dialog window (Fig. V/40) the name of the neuron definition file isshown.The change in sizes are entered as a multiplication factor. Nothing changes for a factor of one(default); sizes decrease for factors smaller than one and increase if it is larger than one. Spheri-cal and cylindrical compartments may be scaled separately.

Spheres: diameters multiplied by: scaling factor for spherical compartments.Cylinders: diameters multiplied by: scaling factor for the diameter of cylindricalcompartments.lengths multiplied by: scaling factor for the length of cylindrical compartments.

Ionic Currents…The command is enabled if the front window is a neuron or compartment definition window. Itcombines ionic current subdefinition formulation and editing functions. See the ‘Nodus Files’section in chapter IV for a description of subdefinitions and their relation to neuron definitionfiles.

Fig. V/44: the Ionic Currents dialog window.

The Ionic Currents dialog window (Fig. V/44, V/45) contains several sections, some are notalways functional. At the left side of the dialog window are the data specific for a selected ioniccurrent subdefinition; at the right are general subdefinition controls and info.The OK and Cancel buttons at the right bottom have their usual meaning and close the dialogwindow, except during the execution of subdefinition management commands (see further).Subdefinition name and data:At the top left of the subdefinition dialog window is a subdefinition selection popup menulabeled as Currents subdefinition, it contains a list of the names of all the ionic current sub-definitions in memory (from all the neuron definition files in memory). The popup menu allowsselection of a subdefinition to view or edit, any changes made to the presently shown ion currentsubdefinition will be stored into memory. If the neuron definition file in the front window usesionic current subdefinitions, the name of the first one used will be shown; if no ionic currentsubdefinitions are in memory None will be shown.Under the subdefinition selection popup menu a data box shows the ionic current subdefinition.It can contain up to 10 different currents, each consisting of a link to a conductance definitionfile, a maximum conductance and a reversal potential (see Eq. 27, chapter III). A current isdefined if a conductance name is shown and text boxes for the maximum conductance and thereversal potential are present (lines #1 to 4 in Fig. V/44), else None is shown (lines #5 to 10).Leak stands for a ionic current with a constant conductance (not voltage-dependent, Eq. 24).

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Conductance: the conductance is selected with a conductance popup menu, which showsthe names of all conductance definition files in memory and Leak. Use the popup menu toselect a conductance equation for the current, each conductance definition can be selected onlyonce in a ion current subdefinition.Gmax: is the maximum conductance (g-bar in Eq. 24-26). It can be expressed either as acompartment membrane surface dependent value in mS/cm2 (millisiemens per square centi-meter) or an absolute value in nS (nanosiemens), depending on the setting of the conductanceoptions in the Preferences dialog when the ion current subdefinition was created (the unitscannot be changed afterwards). Gmax should be larger than zero.Rev. Pot.: the reversal potential (Ej in Eq. 27) in millivolts (mV).

Used by list:Used by shows a list of all neuron definition files in memory using the selected ionic currentsubdefinition. This list is important if the Multiple use of subdefinitions check box in thePreferences dialog is switched on; possible interactions of changes to currents with neurondefinitions that should not change can be judged.Subdefinition management:Four buttons at the lower right perform general ionic current subdefinition management. Theyare used to change the subdefinition list as shown in the subdefinition selection popup menu,they do not affect the data content of the subdefinition. During the execution of New, Dupli-cate or Rename commands all subdefinition management buttons are dimmed and the OK andCancel buttons at the right bottom have a special meaning; they will not close the dialogwindow, but confirm or cancel the command being executed.

New: creates a new ionic current subdefinition. The contents of the data box are cleared anda name for the new subdefinition is requested (Fig. V/45). Change the Untitled name tosomething more relevant and press the carriage return key or the OK button to confirm thenew name, the data box will show 10 empty current lines (None).

Fig. V/45: the Ionic Currents dialog window during a New command.

Delete: deletes irreversibly the presently shown ion current subdefinition. If the sub-definition is Used by a neuron definition file a warning will ask for a confirmation of thecommand. The data are cleared from memory and its name is deleted from the subdefinitionselection popup menu. The Ionic Currents dialog will show another ionic current sub-definition or None if no other subdefinitions are in memory.Duplicate: creates a new ionic current subdefinition with the same currents as in the present-ly shown subdefinition. The contents of the data box are cleared and a name for the newsubdefinition is requested (as in Fig. V/45, but the name will be Copy of…). Edit the nameand press the carriage return key or the OK button, the data box will return to normal.

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Rename: renames the presently shown ionic current subdefinition. The contents of the databox are cleared and the old name of the existing subdefinition is shown (as in Fig. V/45, butwith the original name). Edit the name and press the carriage return key or the OK button, thedata box will return to normal.

Transmitter Release…The command is enabled if the front window is a neuron or compartment definition window.It combines transmitter release subdefinition formulation and editing functions. See the ‘NodusFiles’ section in chapter IV for a description of subdefinitions and their relation to neuron defi-nition files.

Fig. V/46: the Transmitter Release dialog window. At the right the transmitter databoxes for different types of variable transmitter release are shown.

The Transmitter Release dialog window (Fig. V/46, V/47) contains several sections, someare not always functional. At the left side of the dialog window are the data specific for aselected transmitter release subdefinition; at the right are general subdefinition controls and info.The OK and Cancel buttons at the right bottom have their usual meaning and close the dialogwindow, except during the execution of subdefinition management commands (see further).Subdefinition name and data:At the top left of the subdefinition dialog window is a subdefinition selection popup menulabeled as Transmitter subdefinition, it contains a list of the names of all the transmitterrelease subdefinitions in memory (from all the neuron definition files in memory). The popupmenu allows selection of a subdefinition to view or edit, any changes made to the presentlyshown transmitter release subdefinition will be stored into memory. If the neuron definition filein the front window uses transmitter release subdefinitions, the name of the first one used willbe shown; if no transmitter release subdefinitions are in memory None will be shown.Under the subdefinition selection popup menu a data box shows the transmitter release sub-definition. The fields shown depend on the type of transmitter release selected (Fig. V/46).

Potential threshold: transmitter will be released whenever the membrane voltage of thecompartment to which this subdefinition is tied, is larger than or equal to the potentialthreshold (Eth in Eq. 29-31, chapter III) in millivolts (mV). If the membrane voltage is belowthe potential threshold, transmitter release is zero. This value is very important for constanttransmitter release, because it determines when the type 1 synapse connected to it will fire.For variable transmitter release it can improve integration speed; below this value notransmitter release will be computed (otherwise Nodus might compute a zero or infinitelysmall release every minor time step).Base amount: fixed amount of transmitter released (constant release) or minimum amount oftransmitter released (variable release). The base amount (b) is further defined in Eq. 29-31,chapter III.

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Constant transmitter release: select this option to have one pulse of constant transmitterrelease every time the membrane voltage crosses the potential threshold. Connect to a type 1synapse.

Variable transmitter release:Linear to presynaptic potential: select this option to have a simple model of variable,graded transmitter release (Eq. 29). Connect to a type 2 synapse.

Factor: difference between membrane voltage and potential threshold (in mV) is scaled bythis value (f in Eq. 29) to get the amount of transmitter released at every minor time step.Should be larger than zero.

Exponential to presynaptic potential: select this option to have a more complex modelof variable, graded transmitter release (Eq. 30). Connect to a type 2 synapse.

Factor: the exponential is scaled by this value (f in Eq. 30) to get the amount of transmitterreleased at every minor time step. Should be larger than zero.Characteristic potential: difference between membrane voltage and potential threshold(in mV) is divided by this value (c in Eq. 30) before the exponential is computed. Shouldnot be zero.

Process dependent: select this option to supply your own model of variable, graded trans-mitter release (Eq. 31). Not implemented in Nodus 3.1.

Used by list:Used by shows a list of all neuron definition files in memory using the selected transmitterrelease subdefinition. This list is important if the Multiple use of subdefinitions check boxin the Preferences dialog is switched on; possible interactions of changes to transmitterequations with neuron definitions that should not change can be judged.Subdefinition management:Four buttons at the lower right perform general transmitter release subdefinition management.They are used to change the subdefinition list as shown in the subdefinition selection popupmenu, they do not affect the data content of the subdefinition. During the execution of New,Duplicate or Rename commands all subdefinition management buttons are dimmed and theOK and Cancel buttons at the right bottom have a special meaning; they will not close thedialog window, but confirm or cancel the command being executed.

New: creates a new transmitter release subdefinition. The contents of the data box arecleared, buttons are dimmed and a name for the new subdefinition is requested (as in Fig.V/47, but the name will be Untitled). Change the Untitled name to something morerelevant and press the carriage return key or the OK button to confirm the new name, the databox will return to normal.

Fig. V/47: the Transmitter Release dialog window during a Duplicate command.

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Delete: deletes irreversibly the presently shown transmitter release subdefinition. If the sub-definition is Used by a neuron definition file a warning will ask for a confirmation of thecommand. The data are cleared from memory and its name is deleted from the subdefinitionselection popup menu. The Transmitter Release dialog will show another transmitterrelease subdefinition or None if no other subdefinitions are in memory.Duplicate: creates a new transmitter release subdefinition with the same transmitter equationsas in the presently shown subdefinition. The contents of the data box are cleared, buttons aredimmed and a name for the new subdefinition is requested (Fig. V/47). Edit the Copy of…name and press the carriage return key or the OK button, the data box will return to normal.Rename: renames the presently shown transmitter release subdefinition. The contents of thedata box are cleared, buttons are dimmed and the old name of the existing subdefinition isshown (as in Fig. V/47, but with the original name). Edit the name and press the carriagereturn key or the OK button, the data box will return to normal.

Synaptic Currents…The command is enabled if the front window is a neuron or compartment definition window.

Fig. V/48: the Synaptic Currents dialog window. At the right the synapticconductance data boxes for different types of synaptic currents are shown.

The Synaptic Currents dialog window (Fig. V/48-V/50) contains several sections, some arenot always functional. At the left side of the dialog window are the data specific for a selectedsynaptic current subdefinition; at the right are general subdefinition controls and info.The OK and Cancel buttons at the right bottom have their usual meaning and close the dialogwindow, except during the execution of subdefinition management commands (see further).Subdefinition name and data:At the top left of the subdefinition dialog window is a subdefinition selection popup menulabeled as Synaptic subdefinition, it contains a list of the names of all the synaptic currentsubdefinitions in memory (from all the neuron definition files in memory). The popup menuallows selection of a subdefinition to view or edit, any changes made to the presently shownsynaptic current subdefinition will be stored into memory. If the neuron definition file in thefront window uses synaptic current subdefinitions, the name of the first one used will be shown;if no synaptic current subdefinitions are in memory None will be shown.Under the subdefinition selection popup menu a data box shows the synaptic current sub-definition. The fields shown depend on the type of transmitter release selected (Fig. V/48).

Reversal potential: reversal potential of the synaptic current (Es in Eq. 37, chapter III) inmillivolts (mV).

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Peak conductance: is the maximum conductance (g-bar in Eq. 32-36). It can be expressedeither as a compartment membrane surface dependent value in mS/cm2 (millisiemens persquare centimeter) or an absolute value in nS (nanosiemens), depending on the setting of theconductance options in the Preferences dialog when the synaptic current subdefinition wascreated (the units cannot be changed later). It should be larger than zero.

Synaptic conductance:Five different types of synaptic conductance equations are available. These determine whetherthe subdefinition is a type 1 or a type 2 synapse (see the ‘Simulation of Synapses and Connec-tions’ section in chapter III).

Constant: select this option to let the synaptic conductance always be equal to the Peakconductance. Is a type 2 synapse (Eq. 32).‘Alpha’ function: select this option for a standard, simple model of a variable synapticconductance. Is a type 1 synapse (Eq. 33).

Time to peak: determines time course of the synaptic conductance (τ in Eq. 33). Unit ismilliseconds (ms), should be larger than zero.Factor alpha: determines sharpness of change in synaptic conductance (α in Eq. 33).Should be one or larger (integer value).

‘Dual exponential’ function: select this option for a simple model of a variable synapticconductance. Is a type 1 synapse (Eq. 34).

Open time constant: determines initial time course of the synaptic conductance (τo in Eq.34). Unit is milliseconds (ms), should be larger than zero.Close time constant: determines final time course of the synaptic conductance (τc in Eq.34). Unit is milliseconds (ms), should be larger than zero and different from the Opentime constant.

Conductance dependent: select this option to use a voltage dependent conductanceequation for the synaptic conductance (Eq. 35).

Conductance: choose a conductance definition file with the conductance popup menu. Isa type 2 synapse.

Process dependent: select this option to supply your own model of variable synapticconductance (Eq. 36). Not implemented in Nodus 3.1.

Plot time course: this button is enabled when a type 1 synaptic conductance is selected. Asmall diagram showing the time course of the synaptic conductance, normalized to the range 0to 1, is drawn in the Used by box, which is renamed to Conductance plot (Fig. V/49).One (alpha function) or 2 red bars (dual exponential function) at the bottom show the Timeto peak or Open and Close time constants. At the top right the time to peak (in ms) isshown. Several Conductance plots can be superimposed.

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Fig. V/49: the Synaptic Currents dialog window after a Plot time coursecommand.

Used by list:Used by shows a list of all neuron definition files in memory using the selected synapticcurrent subdefinition. This list is important if the Multiple use of subdefinitions checkbox in the Preferences dialog is switched on; possible interactions of changes to synapticconductance equations with neuron definitions that should not change can be judged.The Used by box is replaced by a Conductance plot after the Plot time course button ispressed (see above). The Used by box will be shown again if another synaptic current sub-definition is selected.Subdefinition management:Four buttons at the lower right perform general synaptic current subdefinition management.They are used to change the subdefinition list as shown in the subdefinition selection popupmenu, they do not affect the data content of the subdefinition. During the execution of New,Duplicate or Rename commands all subdefinition management buttons are dimmed and theOK and Cancel buttons at the right bottom have a special meaning; they will not close thedialog window, but confirm or cancel the command being executed.

New: creates a new synaptic current subdefinition. The contents of the data box are cleared,buttons are dimmed and a name for the new subdefinition is requested (as in Fig. V/50, butthe name will be Untitled). Change the Untitled name to something more relevant andpress the carriage return key or the OK button to confirm the new name, the data box willreturn to normal.Delete: deletes irreversibly the presently shown synaptic current subdefinition. If the sub-definition is Used by a neuron definition file a warning will ask for a confirmation of thecommand. The data are cleared from memory and its name is deleted from the subdefinitionselection popup menu. The Synaptic Currents dialog will show another synaptic currentsubdefinition or None if no other subdefinitions are in memory.Duplicate: creates a new synaptic current subdefinition with the same conductance equationsas in the presently shown subdefinition. The contents of the data box are cleared, buttons aredimmed and a name for the new subdefinition is requested (as in Fig. V/50). Edit the Copyof… name and press the carriage return key or the OK button, the data box will return tonormal.Rename: renames the presently shown synaptic current subdefinition. The contents of thedata box are cleared, buttons are dimmed and the old name of the existing subdefinition isshown (Fig. V/50). Edit the name and press the carriage return key or the OK button, thedata box will return to normal.

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Fig. V/50: the Synaptic Currents dialog window during a Rename command.

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Conductance Menu

WindowsConductance definition windowA conductance definition window shows the conductance equations. All ion conductances aredefined by Hodgkin-Huxley like equations, see the ‘Excitable Membrane Models’ section inchapter III for more information. The name of the corresponding file is the title of the windowand is listed at the bottom of the Conductance menu. A check mark before that menu itemshows whether the window is shown, the command can be used to select or open the window.

Fig. V/51: the conductance definition window. Initially the activation (M) equations areshown, press on the H button to see the inactivation rate factors (right).

The conductance definition window (Fig. IV/11, V/51) shows at the top the general conductanceequation which can be of 2 forms:

2 states: the ion channel can be closed or open; this is controlled by the activation factor M(Eq. 25 in chapter III).3 states: the ion channel can be closed, open, or inactivated; this is controlled by the acti-vation factor M and the inactivation factor H (Eq. 26).

Both factors may be raised to a power (4 for activation and 1 for inactivation in Fig. V/51).Below the general equation the (in)activation factor equation (Eq. 21) and the rate factor equa-tions (Eq. 20) are shown. Only one set of equations can be shown at a time, either activation orinactivation. Press the M-button to see the activation equations or the H-button to see the inacti-vation equations (Fig. V/51).The rate factor equations and the power values above M and H are text boxes so that the valuescan be changed to get equations specific for different conductances. Under the equations isspace for a Comment describing the conductance.Conductance Plot window

Fig. V/52: a Conductance Plot window showing activation (M) and inactivation (N) versusvoltage.


The Plot… command can make several types of Conductance Plot windows (Fig. V/52).These windows are graphics windows and do not activate the Conductance menu.The name of each trace in the Conductance Plot window is shown in the correct color in theupper right corner. On black and white monitors the traces will be drawn with differentpatterns.

Menu CommandsThe Conductance menu commands are active when a conductance definition window is thehighlighted window. At the bottom of the menu all conductance definition files in memory arelisted.

Fig. V/53: the Conductance menu.

Plot Scales…The command is enabled if the front window is a conductance definition window. It sets theranges for the potential and value axes of all Conductance Plot windows created by subsequentPlot… commands.

Fig. V/54: the Plot Scales dialog window with automatic (right) or manual (left)control of the value axis range (right).

Potential range: range for the potential axis (X-axis) of new Conductance Plot windows.In millivolts (mV), default is -80 to +40 mV.Value range: range for the value axis (Y-axis) of new Conductance Plot windows. Defaultis automatic; the Plot… command will compute the maximum value in the selectedPotential range and draw an value axis from zero to the maximum value. If the automaticbutton is not selected, 2 text boxes (…to…U, Fig. V/54 right) are shown and the user canset other ranges for the value axis. Use this manual option to enlarge parts of the plot.Overlay existing plots: if a Conductance Plot window of the active conductance and of theselected value type already exists, the Plot… command will draw a new plot in the existingwindow. Useful to visualize the effect of changes in the conductance equations. Default isoff, each Plot… command will create a new window. The Overlay option is not imple-mented in Nodus 3.1.

Plot ConductancePlots the steady state conductance (G∞) versus membrane potential in a Conductance Plotwindow. The axis ranges shown depend on the setting of the Plot Scales command.

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Plot (In)ActivationPlots the steady state activation factor (M∞) and (if present) the steady state inactivation factor(H∞) versus membrane potential in a Conductance Plot window. The axis ranges shown dependon the setting of the Plot Scales command.

Plot Time Constants

Plots the activation time constant (τM) and (if present) the inactivation time constant (τH) versusmembrane potential in a conductance plot window. The axis ranges shown depend on thesetting of the Plot Scales command.

Plot Rate FactorsPlots the activation rate factors (am and bm) and (if present) the inactivation rate factors (ah andbh) versus membrane potential in a Conductance Plot window. The axis ranges shown dependon the setting of the Plot Scales command.


Nodus Examples VI -105

VI. USING THE EXAMPLES

The Nodus Master disk contains several example files to demonstrate how to use Nodus 3. Thischapter describes the content and use of these files.

Demo FilesDemo files are based on extremely simple models, which do not attempt to be realistic. Examinethese files to learn more about using Nodus 3. Suggested user actions have been underlined.Only the output of the simulations is shown here, refer to chapter V for figures showing themenu command dialog windows. Some files show the speed and accuracy of integration.Others demonstrate basic biophysical processes, for example saturation of synaptic current.The demo files are described in an order optimal for discovering Nodus. Their names reflect adifferent, older sequence of numbering which has been retained for compatibility with Nodus 2.

Test-cell 2 DemoTest-cell 2 Demo is a simulation data file, it contains data for the simulation of a simpleexperiment in a 5-compartment model with 3 voltage dependent ion currents in one of thecompartments.Select the Open Simulation command in the File menu, open the ‘Nodus 3 Data’ folder onthe Nodus Master disk and double-click on the ‘Test-cell 2 Demo’. Nodus loads the simulationdatabase and the linked neuron definition file (‘Test-cell 2’) and conductance definition files(‘CS Fast Na Current’, ‘CS Delayed Rectifier’ and ‘CS A Current’). These file names are listedat the bottom of the respective file menus, press on the Neuron menu title to look at the bottomof the menu and then release, repeat with the Conductance menu.Two simulation windows are created: a simulation plot window, titled ‘Test-cell 2Demo.Sim0000’ and a time window (Fig. VI/1). Because a simulation window is active mostof the Simulation menu commands are enabled, press on the Simulation menu title to look atthe menu and then release.

Fig. VI/1: the simulation plot and time windows of the ‘Test-cell 2 Demo’ simulation.

The simulation Plot Window title consists of the file name, followed by ‘.Sim’ and the simu-lation number (it may be larger than zero if Nodus has already been used) The window has beenconfigured to show three plots: the upper axis shows the membrane voltage in compartment #1(the soma of Test-cell 2), the middle axis shows the ion currents in compartment #2 (the spikeinitiation zone): the ‘CS Fast Na Current’, the ‘CS Delayed Rectifier’ and the ‘CS A Current’,the lower axis shows the current injected into the soma (compartment #1).

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Select the Run command in the Simulation menu to start the simulation. After a slight delay(used by the hybrid integration method to generate an equations table for the conductancefactors), the simulation will start (Fig. VI/2). Initially a small current is injected to make the cellfire, after 600 ms a ramp current with a period of 200 ms is injected. The initial hyper-polarization during the ramps removes the inhibition of the A-current, which is much increasedduring the spike in the depolarization phase of the ramp (middle axis, green trace). Let it Runtill it finishes after 1000 milliseconds (ms) of simulated action potentials.

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Fig. VI/2: the plot output of the ‘Test-cell 2 Demo’ simulation after a complete Run.

This demo demonstrates several features of Nodus: voltage dependent ion currents, sophisti-cated graphic output, complex current injections, etc. We will now examine how the simulationwas configured. One can view the different settings in the Simulation menu, but not changethem (because this simulation has finished running). This is shown in the Simulation menuby italic menu commands, press on the Simulation menu title to look at the menu and thenrelease. To be able to change the settings a new simulation database has to be created. Selectthe New Simulation command in the File menu, do not change any settings (Fig. V/3, UseTest-cell 2 Demo for New simulation database and Resting potential for Initialvalues) and press OK. A new simulation plot window (‘Test-cell 2 Demo.Sim2’) is created.Select the Measure command in the Simulation menu (Fig. V/29). Measure the peak ampli-tude of the A-current (green trace in the second plot) during the 4 action potentials. Put the haircursor on the top of the action potential (first plot), press the mouse button and move to thesecond action potential to measure the period of firing. Close the Measure window to finish.Select the Integration Settings command in the Simulation menu (Fig. V/20). Examine theTime controls and Integration method settings, change them if you like to, and press OKor Cancel if you want to undo any changes.Select the Configure Plots command in the Simulation menu (Fig. V/22). Examine thesettings for the different axes by pressing the Edit axis # 1, 2 or 3 buttons. Press on thepopup menus to see how values to plot are selected, in particular the compartment popup menus(the third from the left) of axis 1 and 3 and the subdefinition popup menus (fourth from the left)of axis 2. Press OK when you have finished.


Suggested exercises: add plots for membrane potential in all compartments (Edit axis #1,change compartment popup menu in first popup row to #1 Soma, press on the neuron popupmenu (first at left) of the second row and change from not used to Test-cell 2, change thecompartment popup menu to #2 main segment, repeat for rows 3 to 5 and set to compart-ments #3 to #5); add a fourth axis showing A-current activation and inactivation (add an axis bypressing on the fifth icon above the Edit axis # row and Edit axis #4, change the valueaxis popup menu to conductance (in)activation with a range from 0.0 to 1.0 U, selectTest-cell 2 on the first popup row neuron popup menu and set the subdefinition menu to #1CS A Current:M, repeat for second popup row and set to #1 CS A Current:H, note thatthere is not enough space to show the complete titles of the popup menus). You can Run thesimulation to watch the effect of the new Configure Plot settings; make a New Simulationagain after it has finished running.Reset to the original ‘Test-cell 2 Demo’ simulation database. If the present simulation has notfinished running you will have to select the Close… command in the File menu (you mightwant to press on the option key, this will conserve the graphics in the Plot Window). OpenSimulation the ‘Test-cell 2 Demo’ file again.Select the Current Clamp command in the Simulation menu (Fig. V/25). Examine thesettings for current injections in the Soma of Test-cell 2 by pressing on the Next andPrevious buttons. Press OK when you have finished. Suggested exercises: remove the rampcurrents and change the amplitude of the steady current to see how it affects spiking frequency(press Next till Current #3 is shown and press Delete, press Delete again for Current #2,change End of Current #1 to 1000 ms and change Amplitude and press OK , R u nSimulation, New Simulation and repeat for another Amplitude of Current #1, examinefrequencies for amplitudes ranging from 0 to 1.0 nA); change steady current to current pulses(after preceding exercise, set Current #1 to Cyclical, set Period to 250 ms and On from 0to 50 ms, press OK and Run Simulation, check effect of changing amplitude).

Test-cell 2 Demo is also used to demonstrate the speed and accuracy of the different integrationmethods available in Nodus. The simulation computation times (in minutes) were measuredwith the Status Window command on a Macintosh II (High multifinder priority inPreferences menu on).

Integration method Time

Rate factor table 0.20Hybrid Euler, ∆V=0.50 mV 1.32Hybrid Euler, ∆V=0.10 mV 2.57Hybrid Euler, ∆V=0.01 mV 18.95Fehlberg, Rel. Err=10-4 2.75

Fehlberg, Rel. Err=10-6 3.43

Fehlberg, Rel. Err=10-8 5.73

It makes little sense to set ∆Vmax in the hybrid Euler method smaller than 0.1; it will computemuch slower and still less accurate than the Fehlberg method with a relative error of 10-4.The accuracy of the different integration methods was tested with the maxima option in theText Output command (table on next page). Remark that the largest inaccuracy in the hybridmethod occurs in the firing period, but the values are equivalent to the results obtained with theFehlberg method. The size of the overshoot (and of the afterhypolarization) are less sensitive tothe integration method.


Integration method Action potential #1 Action potential #2 FiringTime (ms) Size (mV) Time (ms) Size (mV) period (ms)

Hybrid Euler, ∆V=0.50 mV 19.571 36.648 612.584 36.313 593.013Hybrid Euler, ∆V=0.10 mV 19.665 36.620 613.553 36.296 593.888Hybrid Euler, ∆V=0.01 mV 19.721 36.622 615.231 36.314 595.510Fehlberg, Rel. Err=10-4 19.745 36.604 616.682 36.322 596.937Fehlberg, Rel. Err=10-6 19.747 36.604 616.710 36.322 596.963Fehlberg, Rel. Err=10-8 19.728 36.604 616.704 36.322 596.976

Test-cell 2‘Test-cell 2’ is the neuron definition file that contains the description of the simple invertebrateneuron model used in ‘Test-cell 2 Demo’. It is a 5-compartment model (corresponding to 8compartments when the weight factors are expanded, Fig. VI/3) with 3 voltage dependent ioncurrents in one of the compartments (the spike initiation zone). This model is an example of theuse of ionic currents, of node connections and branch connections and of weight factors in aneuron model definition, it is not a good model for a real neuron.

12 3

4

4

5

5 5

Fig. VI/3: diagram of Test-cell 2. The compartment numbers are shown, compartment#2 has ionic currents.

If you have Open Simulation the ‘Test-cell 2 Demo’ the ‘Test-cell 2’ neuron definition file isalready in memory, select the corresponding menu item at the bottom of the Neuron menu,otherwise use the Open Neuron command in the File menu to open ‘Test-cell 2’. The neurondefinition dialog window (Fig. V/33) shows the number of compartments and the cable para-meters used in this model.Select the Next Compartment command in the Neuron menu to look at the ‘Soma’ compart-ment (#1). A compartment definition dialog window (Fig. V/34) is shown, look at thedescription of the spherical morphology (top). The connection to compartment #2 is shown inthe box at the bottom left; this is a parent compartment, the link is not dimmed. Select the NextCompartment command again to look at compartment #2 (main segment). This is a cylindricalcompartment (top right). Look at the connections (bottom left); the first is dimmed because thisis the child compartment for the connection to the Soma; other links are enabled becausecompartment #2 is the parent to 4 other connections in the model; note that the first 2 are nodeconnections with no weight factors (1) and the last 2 are branch connections with weight factors.Compartment #2 is excitable; it has a tie to an ionic currents subdefinition, shown by theConnor Stevens Currs popup menu title at left. Use the Ionic Currents command in theNeuron menu to look at the subdefinition, press Cancel when done.


Look at one of the conductance definition files used by this ionic currents subdefinition. Selectthe CS A Current conductance definition window (Fig. V/51) by choosing the correspondingmenu item in the Conductance menu. Look at the activation factor equations, press H to lookat the inactivation factor equations. Select the Plot (In)Activation command in theConductance menu to get a Conductance Plot window.Select the ‘Test-cell 2’ compartment definition window (it is listed at the bottom of the Neuronmenu) and look at the other 3 compartments with the Next Compartment command.Suggested exercises examine how changing the model affects the ‘Test-cell 2 Demo’ simulation.Be sure to have an old (Closed) simulation database in memory (see description of that demo).Change the maximum conductances of the ionic currents to see how spiking threshold andfrequency is affected (select the ‘Test-cell 2’ neuron or compartment definition window, selectthe Ionic Currents command and change one or several Gmax values, press OK, select theNew Simulation command, click on the compile form Test-cell 2 button in the Newsimulation database section, click on the Values in memory button in the Initial valuessection, press OK, Run the simulation); increase the length of the axon and see how spikingthreshold and frequency is affected (select the ‘Test-cell 2’ neuron definition window with theParameters command in the Neuron menu, increase the number of compartments to 6, selectthe Go to Compartment # command in the neuron menu, type 3 and press OK, add aconnection to compartment 6 by typing a 6 in the first text box on the second line of the connec-tions list, Go to Compartment # 6 and type a Diameter of 5 µm and a Length of 300 µm,make a New Simulation and Run it); change a conductance equation (select the conductanceequation window, press on the H and change the 56.00 number in the am equation to46.00,make a New Simulation and Run it).

Test-cell 6 Demo 1This simulation run file demonstrates temporal summation of two IPSPs, shown at all 5compartments in a simple neuron model (Fig VI/4).

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-1.0

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Fig. VI/4: the plot output of the ‘Test-cell 6 Demo 1’ simulation after a complete Run.


Two IPSPs are fired in compartment 5. The upper plot shows the membrane potentials, thelower left plot synaptic conductances and the lower right plot synaptic currents. Note that in all3 plots traces with the same color refer to the same compartment. The synaptic current saturatesduring the second IPSP, while the conductances are for the two IPSPs are identical.Open Simulation ‘Test-cell 6 Demo 1’ (this is possible only if the previous simulation hasfinished Running or is Closed) and Run it. Do New Simulation and examine the Inte-gration Settings and Configure Plots dialog windows.Examine the Synaptic Firing Times command dialog window (Simulation menu, FigV/27) to see how the 2 IPSPs are fired.Suggested exercises: fire a third IPSP (select the Synaptic Firing Times command, type 18in the third Firing times edit box, press OK, do New Simulation (with Use Test-cell 6Demo 1 for New simulation database) and Run it); fire IPSPs in another compartment(select the Synaptic Firing Times command, press the Delete all firing times button,select another compartment and synapse with the compartment popup menu (third from the left),type in the Firing times, press OK , do New Simulation and Run it); examine spatialsummation by firing synapses in different compartments together.

Test-cell 6This neuron definition file contains a small 5 passive membrane compartment model that is usedin to demonstrate synaptic currents. The model and the demonstrations in ‘Test-cell 6 Demo 1’are based on Fig. 2 of Perkel, D.H., Mulloney, B., and Budelli, R.W.: Quantitative methodsfor predicting neuronal behavior. Neuroscience 4 (1981) 823-837.If you have Open Simulation the ‘Test-cell 6 Demo 1’ the ‘Test-cell 6’ neuron definition fileis already in memory, select the corresponding menu item at the bottom of the Neuron menu,otherwise use the Open Neuron command. A neuron definition dialog window is made.Each compartment has one synapse: #1 to #4 are excitatory (‘Fast EPSP’ and ‘Slow EPSP’), #5is inhibitory (‘Slow IPSP’). Select the Synaptic Currents command (Neuron menu) to lookat the synaptic currents subdefinitions (Fig V/48). Only one subdefinition is shown, use theSynapse subdefinition popup menu at the top left to see another subdefinition and the Plottime course button to see a fast graphic display of the synaptic conductance, press Cancelwhen done.Suggested exercises: change the time constants of a synaptic conductance (Synaptic Currentscommand, select the Slow IPSP Synapse subdefinition, press Plot time course,change the close time constant to 1.2, press Plot time course, press OK, do New Simu-lation (with Compile from Test-cell 6 1 for New simulation database) and Run it);add voltage dependent ionic currents and see whether a spike is triggered after an EPSP (Go toCompartment #1, (the ‘Test-cell 2’ file must be in memory), do Ionic Currents and selectthe Connor Stevens Currs (if not shown), press Duplicate and OK, select the Copy ofConnor Stevens Currs in the ion currents popup menu of the compartment definitionwindow of Test-cell 6,do New Simulation (with Compile from Test-cell 6 1 for Newsimulation database) and Run it).

Test 7 Network DemoThis simulation run file shows reciprocal inhibition between 2 neurons in a small network (FigVI/5). The difference in firing frequency is controlled by current injection (Current Clamp).Open Simulation ‘Test 7 Network Demo’ (this is possible only if the previous simulation hasfinished Running or is Closed) and Run it. Do New Simulation and examine the Config-ure Plots and Current Clamp dialog windows; remark the use of the neuron definitionpopup menus (the first one in the row) to distinguish between the 2 neurons in the network.


Test 7 NetworkThis network definition file models an extremely simple network of 2 small neurons.If you have Open Simulation the ‘Test 7 Network Demo’ the ‘Test 7 Network’ networkdefinition file is already in memory, select the corresponding menu item at the bottom of theNetwork menu, otherwise use the Open Network command in the File menu to open ‘Test7 Network’. The network definition dialog window (Fig. V/30) is shown.

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Fig. VI/5: the plot output of the ‘Test 7 Network Demo’ simulation after a completeRun.

Examine the synaptic connections between the 2 neurons with the Synaptic Connectionscommand in the Network menu (Fig. V/32). Use the Connections from popup menu tolook at the connections from the Left cell and from the Right cell.Suggested exercises: change the (axonal) delay between the 2 neurons (Synaptic Connec-tions command, change the Delay from the Left cell to Right cell to 50 ms, press OK, doNew Simulation (with Compile from Test 7 Network for New simulation data-base) and Run it); add a third neuron to the network (select the network definition window,type ‘Middle cell’ for Local name in the third row and select Test-cell 7 in the neurondefinition popup menu, use the Synaptic Connections command to connect both the Leftcell and Right cell to the Middle cell by changing the Neuron popup menu in the secondrow from None to Middle cell and press OK, do New Simulation and Run it).

Test-cell 7This neuron definition file is similar to the ‘Test-cell 2’ model, with a synapse added to thecompartment #4 dendrite and a transmitter release site to the compartment #5 dendrite.Use the Transmitter Release and Synaptic Currents commands in the Neuron menu toexamine the equations used, both the dialog windows are very similar.


Test-cell 1 ClampThis simulation data file illustrates the use of voltage clamps. It reproduces the experiment thatshows the existence of an A-current: a long hyperpolarization followed by a small depolarization(as in Fig. 1B of Connor JA and Stevens CF: Voltage clamp studies of a transient outwardmembrane current in gastropod neural somata. J. Physiol. (London), 213 (1971) 21-30.).

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Fig. VI/6: the plot output of the ‘Test-cell 1 Clamp’ simulation after a complete Run.

The upper axis shows the membrane voltage in the unique compartment, the middle axis theclamping current and the lower axis activation and inactivation factors for the A-current (‘CS ACurrent’). The mechanism of A-current activation is demonstrated: a long hyperpolarizationslowly removes inactivation, depolarization instantly raises the activation factor.Use the Voltage Clamp command Configure Plots in the Simulation menu to examinehow this simulation has been constructed.

Test-cell 1This neuron definition file contains the model that Connor and Stevens used to simulate the ioncurrents in an Anisidoris neuron (Connor JA and Stevens CF: Prediction of repetitive firingbehaviour from voltage clamp data on isolated neurone soma. J. Physiol. (London), 213 (1971)31-53.). It is extremely simple: one excitable compartment, therefore it is only useful for ioncurrent simulations. The ion conductance equations are contained in the 3 ‘CS…Current’conductance definition files.

Test-cell 3This neuron model was used to test the accuracy of the Nodus integration methods. The 27compartment model (with many weight factors) is equivalent to a linear cable model of a spinalα-motoneuron for which the analytical results are known (Rall, W: Branching dendritic treesand motoneuron membrane resistivity. Exp. Neurol., 1 (1959) 491-527; Segev, I, Fleshman,JW, Miller, JP and Bunow, B: Modeling the electrical behavior of anatomically complex neu-rons using a network analysis program: passive membrane. Biol. Cybern., 53 (1985) 27-40.).


The input resistance (RN) of the model was measured with a hyperpolarizing current injection,

the time constant (τm) and electrical length (L) were calculated from exponential peeling data(Nodus 2).

Integration method RN τmL

MΩ % Err ms % Err %Err

Analytical 1.57 7.00 1.46Hybrid Euler, ∆V=0.1 mV 1.51 -4 7.53 +8 1.58 +8Fehlberg, Rel. Err=10-4 1.51 -4 7.09 +1 1.51 +3Fehlberg, Rel. Err=10-8 1.51 -4 6.98 +0 1.52 +3

The results of this accuracy evaluation compare favorably with other compartmental simulationprograms (Segev et al, 1985).

Test-cell 4a and 4b‘Test-cell 4a’ and ‘Test-cell 4b’ are two different models of the same (extremely simple) neuron.‘Test-cell 4a’ uses only node connections, while in ‘Test-cell 4b’ compartments have been fusedand some compartments have branch connections (8 compartments instead of 13).These models were used to check the accuracy of branching versus node connections. A -1.0nA, 1000 ms current was injected in the soma (compartment #1, Nodus 2, double precisionFehlberg method, relative error was 10-8):

Model ∆V (mV) in compartment #1 4 5 8-10/7

4a -45.62 -44.38 -43.99 -43.234b -45.71 -44.25 -43.86 -42.85Error (%) -0.20 +0.29 +0.30 +0.88

The errors caused by the use of branch connections are minimal, both in the soma and in thebranch compartments! A -0.5 nA, 1000 ms current was injected in a branch (compartment #5,Nodus 2, double precision Fehlberg method, relative error is 10-8):

Model ∆V (mV) in compartment #1 4 5 8-10/7

4a -21.99 -25.17 -44.67 -22.424b -21.93 -26.99 -46.47 22.34Error (%) +0.27 -7.23 -4.03 +0.36

There is a significant error in all compartments of the side-branch in which current was injected,but in the other compartments, including the soma, the error is minimal. This example showsthat a branch connection is sufficiently equivalent to a node connection at the center of the (split)parent compartment for most experiments.

Test-cell 5a, 5b and 5c‘Test-cell 5a’, ‘Test-cell 5b’ and ‘Test-cell 5c’ are neuron definition files containing threedifferent models of the same example neuron: 5a is the original model and 5b and 5c correspondto the two reduction steps described in the ‘Nodus Implementation of Compartmental Models’section in chapter III, Fig III/6.The loss of accuracy caused by the model reduction was estimated by a -0.5 nA, 400 ms currentin the soma (compartment #1, Nodus 2, double precision Fehlberg method, relative error 10-8):


Model ∆V (mV) in compartment #1 6 8/7/7 18/13/10

5a -19.30 -19.01 -18.95 -18.905b -19.30 -19.01 -18.93 -18.88Error (%) +0.00 +0.00 +0.11 +0.125c -19.45 -19.16 -19.08 -18.98Error (%) +0.78 +0.80 +0.71 +0.45

The errors for current injections in the soma caused by the reduction are reasonably small in allcompartments, including the soma. A -0.2 nA, 400 ms current was injected in a branch (a: compartment #16,17 and 18/b: #11, 12and 13/c:#10, Nodus 2, double precision Fehlberg method, relative error was 10-8).

Model ∆V (mV) in compartment #1 6 8/7/7 18/13/10

5a -22.69 -22.97 -23.70 -25.555b -22.65 -22.94 -23.86 -26.12Error (%) +0.14 +0.14 -0.64 -2.245c -22.78 -23.07 -23.99 -29.87Error (%) +0.42 +0.42 +1.21 +16.95

The errors for current injections in the branches caused by the reduction are reasonably small inthe soma, but totally unacceptable at the reduced side-branches!

Realistic Models

Squid Giant Axon DemoThis simulation data file contains the database for a simulation of the conduction of a classicHodgkin and Huxley action potential (‘HH Fast Na Currents’ and ‘HH Delayed Rectifier’) inthe ‘Squid giant axon’ neuron model. An action potential current is triggered in compartment #1by a 2 µA, 0.2 ms current pulse (see Current Clamp, the plot window (Fig. V/7) shows themembrane potential in several compartments (see Configure Plots).

HH Fast Na Current and HH Delayed RectifierThese conductance definition files contain the equations developed by Hodgkin and Huxley forthe fast inward sodium current and the delayed rectifier. The original equations (Hodgkin ALand Huxley AF: A quantitative description of membrane current and its application to conductionand excitation in nerve. J. Physiol. (London), 117 (1952) 500-544.) were adapted to themodern membrane potential convention (resting membrane potential is -70 mV).

Squid Giant Axon‘Squid Giant Axon’ is a neuron definition file that contains a 10-compartment model of 10 mmof the axon that was modeled by Hodgkin and Huxley (Hodgkin AL and Huxley AF: A quanti-tative description of membrane current and its application to conduction and excitation in nerve.J. Physiol. (London), 117 (1952) 500-544.).All compartments have excitable membrane (the ‘HH Fast Na Currents’ and ‘HH DelayedRectifier’ currents and a leak).


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Fig. VI/7: the plot output of the ‘Squid Giant Axon Demo’ simulation.

CS Fast Na Current, CS Delayed Rectifier and CS A CurrentThese conductance definition files contain the equations for 3 voltage dependent ion currents.The equations are from De Schutter E: Alternative equations for the molluscan ion currentsdescribed by Connor and Stevens. Brain Res., 382 (1986) 134-138; and are derived from thepaper Connor JA and Stevens CF: Prediction of repetitive firing behaviour from voltage clampdata on isolated neurone soma. J. Physiol. (London), 213 (1971) 31-53.


Appendix VI I -117

VII. APPENDIX

Maxima for Memory Storage (Nodus 3.2)†

Simulations:Simulation database:

Maximum number of simulation data files 1Maximum number of simulation parameters in a database 20000Maximum number of integrated variables in a database 4500Maximum size of equation table 100000

Simulation plot:Maximum number of axes 4Maximum number of plots on each axis 6Maximum number of text output parameters 20

Experiments:Maximum number of current clamps 40Maximum number of periods in a voltage clamp experiment 5Maximum number of voltage clamped neurons 2Maximum number of preset synaptic firing times 100Maximum number of ionic conductance blocking periods 1

Networks:Maximum number of network definition files in memory 1Maximum number of neurons in a network 200Maximum number of connections from one presynaptic neuron 60

Neurons:Maximum number of neuron definition files in memory 20Maximum number of compartments in memory:

no 3-dimensional coordinates in use 40003-dimensional coordinates used in any neuron model 3000

Compartments:Maximum number of connections to other compartments:

soma compartment 24other compartments 6

Maximum number of ties to ionic current subdefinitions 1Maximum number of ties to synaptic current subdefinitions 5Maximum number of ties to transmitter release subdefinitions 1Maximum number of ties to pool subdefinitions 2

Subdefinitions:Maximum number of ionic currents in memory 20

maximum number of currents in an ionic current subdefinition 13Maximum number of synaptic currents in memory 60Maximum number of transmitter release sites in memory 60Maximum number of pools in memory 60

Conductances:Maximum number of conductance definition files in memory 20Maximum number of (in)activation factors 2

† The maxima are supplied as guidelines only. It is not guaranteed that these maximum numbers will remainunchanged in future updates of Nodus 3.

VI I -118 Appendix

Shift and Option Key Menu Modifications

Only menu commands which can be modified are shown. Press the shift key and/or option keybefore selecting the menu command to get the required modification.

No key Shift key Option key Shift+option keysFile menu

New Simulation… New Simulation° New Simulation… New Simulation°Open Open† Open Open†

Close ‘file name’Close ‘file name’

Hide ‘file name’Hide simulation

Close ‘file name’Close simulation‡

Kill ‘file name’Kill ‘file name’

Close All Windows Hide All Windows Hide All Graphs Hide All WindowsNeuron menu

Go to Compartment Go to # Go to Compartment Go to #

° New simulation database uses the old database in memory, initial values are the old values.No dialog window is shown.

† Opens the definition file without creating a definition window. The data are loaded intomemory and the file name is put at the bottom of the appropriate menu.

‡ The simulation database is closed, the plot window remains on the screen.

Appendix VI I -119

Bugs and ProblemsConsult the ftp site (see Nodus 3.2 Update) for recent information about bugs and problems.

VI I -120 Appendix

Import FormatsAn import file is a standard Macintosh TEXT file, i.e. an ASCI file with only a carriage return(CR) at the end of a line (no linefeeds). Most entries are real or integer numbers and are separ-ated by TABs and/or spaces.

GenesisGenesis is a Unix neuron and network simulation program developed at the Claifornia Instituteof Technology. It can read neuron morphology from ‘.p’ files, which are partially supported byNodus.The file consists of comment lines (preceded by ‘//’), control lines (preceded by ‘*’; only‘*symmetric’, ‘*absolute’, ‘*relative’, ‘*spherical’, ‘*cylindrical’, ‘*setglobal CM …’, ‘*set-global RM …’ and ‘*setglobal RA …’ are supported) and compartment lines (one for eachcompartment). The compartment lines should be ordered by their connections (from parent tochild).Nodus reads the 6 first items (separated by TABS or spaces) on compartment lines (channelobjects are not supported) in the format:NAME PARENT X Y Z D.

NAME is the name of the compartment.PARENT is the name of the parent-compartment to which this one is connected (by a nodeconnection) or ‘none’.X, Y and Z are the center coordinates in µm of the distal face of the compartment cylinder anddetermine the compartment length (integer or real format).D is the diameter in µm (integer or real format).

LadderAn extremely simple neuron morphology format. Each compartment is represented by one lineof text, consisting of the diameter in µm and the length in µm, in integer or real format separatedby TABS or spaces.Remark that connections between compartments cannot be specified, Nodus therefore assumesthat they form a long, unbranched “ladder”.

NINDS, NINDS with RmThe format used at the Laboratory of Neural Control, NINDS/NIH (Dr. Burke).The morphology is determined a branch and order number for each part of a dendrite. Branchingis always binary and branch numbers depend on the number of the parent branch: BRchild1=2*BRpar-1 and BRchil d2=2*BRpar. This nomenclature is described in ‘Segev I, Fleshman J W,Miller J P and Bunow B: Modeling the electrical behavior of anatomically complex neuronsusing a network analysis program: passive membrane. Biol. Cybern., 53 (1985) 27-40’.There is one line of data for each segment of a branch, corresponding to one compartment in theneuron model. All entries are integer numbers, separated by TABS or spaces, with as format:ID ORD BR SEG TYPE X1 Y1 Z1 X2 Y2 Z2 LENGTH DIAMETER [RESISTANCE]

ID: dendrite number (between 1 and 32000).ORD: branching order (between 1 and 50)BR: branch number (derived from parent branch, between 1 and 32000).SEG: sequential number when the branch is broken into subsegments (between 1 and 32000).TYPE: 1=next compartment is next segment of same branch.

2=branch point: next compartment is order+1.

Appendix VI I -121

3=termination of branch.X1, Y1, Z1: 3 dimensional spatial coordinates of the start point of the segment.X2, Y2, Z2: 3 dimensional spatial coordinates of the end point of the segment.LENGTH: length of segment in µm*100.DIAMETER: diameter of segment in µm* 100.RESISTANCE: is optional, only in the ‘NINCDS with Rm’ format; specific membrane resis-tance for this segment in kΩ*100.

The file contains the data for all of the dendrites. The file does not have to be ordered, butconversion will go faster if it is. The first line always has to be the description of the soma, withID=0, ORD=0, BR=0, SEG=0 and TYPE=0. The stem of a dendrite has ORD=0, BR=1 andSEG=1.

OxfordThe format used at the Laboratory of Neuropharmacology, University of Oxford (Dr.Somogyi).First line is the number of segment input lines (integer). Segment input lines describe very smallparts of the (EM) measured neuron, several segments may be fused into one compartment in theneuron model. All entries are real numbers (except TYPE which is integer), separated bycommas and spaces, with as format:X,Y,Z,TYPE,D

X, Y, Z: coordinates of the segment in µm.TYPE: segment type code (integer value):

3: start.4: continue.5: fibre swelling, Nodus cosiders this to be the soma circumference.6: spine base.7: branch split.8: branch end.

D: diameter of the segment in µm.If no type code 5 is found in the import file, Nodus will take the first data point as the soma anduse the diameter at this point. This may result in a soma which is too small.Also Oxford requires exact spacing between the data points (Fortran FORMAT type offormatting), an example of a correct line is: -0011.90,00049.80,-0020.00,005,3.50

VI I -122 Appendix

ReferencesA paper describing Nodus 2.3 has been published. Please refer to this paper when publishingresults of modeling with Nodus.E. De Schutter: Computer software for development and simulation of compartmental models of

neurons. Computers in Biology and Medicine 19: 71-81 (1989).

Methodology of compartmental modelingEdwards, D. H., Jr., and B. Mulloney (1984) Compartmental models of electrotonic structure

and synaptic integration in an identified neurone. J. Physiol. (London) 348: 89-113. (use ofexponential peeling method to create 3-compartment models).

Koch, C., and I. Segev (1989) Methods in neuronal modeling: from synapses to networks. MITPress, Cambridge, MA. (very good book, overview of compartmental modeling and otherrealistic modeling methods with extensive examples).

MacGregor, R. J. (1987) Neural and brain modeling. Academic Press, London. (mediocrebook, overview of lots of modeling methods with extensive literature reviews).

Perkel, D. H., and B. Mulloney (1978) Electrotonic properties of neurons: steady-stadecompartmental model. J. Neurophysiol. 41: 621-639. (steady state analysis of compartmentalmodels).

Perkel, D. H., and B. Mulloney (1978) Calibrating compartmental models of neurons. Amer. J.Physiol. 235: 93-98. (steady state analysis of compartmental models).

Perkel, D. H., B. Mulloney, and R. W. Budelli (1981) Quantitative methods for predictingneuronal behavior. Neuroscience 4: 823-837. (good introduction to passive compartmentalmodeling).

Rall, W. (1962) Theory of physiological properties of dendrites. Ann. N.Y. Acad. Sci. 96:1071-1092. (standard compartmental modeling reference!).

Rall, W. (1964) Theoretical significance of dendritic trees for neuronal input-output relations. InNeuronal theory and modeling, Reiss, R. F. , ed., pp. 73-97, Stanford University Press,Stanford. (standard compartmental modeling reference!).

Shelton, D. P. (1985) Membrane resistivity estimated for the Purkinje neuron by means of apassive computer model. Neuroscience 14: 111-131. (extensive analysis of methodology andproblems, comparison with equivalent cylinder model).

Examples of compartmental modeling in invertebrate neurobiologyEdwards, D. H., Jr., and B. Mulloney (1987) Synaptic integration in excitatory and inhibitory

crayfish motoneurons. J. Neurophysiol. 57: 1425- 1445.Getting, P. A. (1974) Modification of neuron properties by electrotonic synapses. I. Input

resistance, time constant, and integration. J. Neurophysiol. 37: 846-857.Miller, J. P., and G. A. Jacobs (1984) Relationships between neuronal structure and function.

J. Exp. Biol. 112: 129-145.Waldrop, B., and R. M. Glantz (1985) Synaptic mechanisms of a tonic EPSP in crustacean

visual interneurons: analysis and simulation. J. Neurophysiol. 54: 636-650.Zucker, R. S. (1972) Crayfish escape behavior and central synapses. III. Electrical junctions

and dendrite spikes in fast flexor motoneurons. J. Neurophysiol. 35: 638-651.

Examples of compartmental modeling in vertebrate neurobiologyClements, J. D., and S. J. Redman (1989) Cable properties of cat spinal motoneurones

measured by combining voltage clamp, current clamp and intracellular staining. J. Physiol.(London) 409: 63-87.

Appendix VI I -123

Fleshman, J. W., I. Segev, and R. E. Burke (1988) Electrotonic architecture of type-identifiedalpha-motoneurons in the cat spinal cord. J. Neurophysiol. 60: 60-85.

Gamble, E., and C. Koch (1987) The dynamics of free calcium in dendritic spines in responseto repetitive synaptic input. Science 236: 1311-1315.

Holmes, W. R., and C. D. Woody (1989) Effects of uniform and non-uniform synaptic‘activation-distributions’ on the cable properties of modeled cortical pyramidal neurons. BrainRes. 505: 12-22.

Knowles, W. D., R. D. Traub, R. K. S. Wong, and R. Miles (1985) Properties of neuronalnetworks: experimentation and modeling of the epileptic hippocampal slice. Trends Neurosci.8: 73-79.

Koch, C. (1985) Understanding the intrinsic circuitry of the cat's lateral geniculate nucleus:electrical properties of the spine-triad arrangement. Proc. Roy. Soc. London Ser. B 225: 365-390.

Llinas, R., and C. Nicholson (1976) Reversal properties of climbing fiber potential in catPurkinje cells: an example of a distributed synapse. J. Neurophysiol. 39: 311-323.

Miller, J. P., W. Rall, and J. Rinzel (1985) Synaptic amplification by active membrane indendritic spines. Brain Res. 325: 325-330.

Nitzan, R., I. Segev, and Y. Yarom (1990) Voltage behavior along the irregular dendriticstructure of morphologically and physiologically characterized vagal motoneurons in theguinea pig. J. Neurophysiol. 63: 333- 346.

Pellionisz, A., and R. Llinas (1977) A computer model of cerebellar Purkinje cells. Neuro-science 2: 37-48.

Perkel, D. H. (1983) Functional role of dendritic spines. J. Physiol. (Paris) 78: 695-699.Perkel, D. H., and D. J. Perkel (1985) Dendritic spines: role of active membrane in modulating

synaptic efficiency. Brain Res. 325: 331-335.Pun, R. Y. K., E. A. Neale, P. B. Guthrie, and P. G. Nelson (1986) Active and inactive central

synapses in cell culture. J. Neurophysiol. 56: 1242-1256.Rall, W., and G. M. Shepherd (1968) Theoretical reconstruction of field potentials and

dendrodendritic synaptic interactions in olfactory bulb. J. Neurophysiol. 31: 884-915.Schierwagen, A. (1986) Segmental cable modelling of electrotonic transfer properties of deep

superior colliculus neurons in the cat. J. Hirnforsch. 27: 679-690.Segev, I., J. W. Fleshman, J. P. Miller, and B. Bunow (1985) Modeling the electrical behavior

of anatomically complex neurons using a network analysis program: passive membrane. Biol.Cybern. 53: 27-40.

Segev, I., and W. Rall (1988) Computational study of an excitable dendritic spine. J.Neurophysiol. 60: 499-523.

Shepherd, G. M., R. K. Brayton, J. P. Miller, I. Segev, J. Rinzel, and W. Ral (1985) Signalenhancement in distal cortical dendrites by means of interactions between active dendriticspines. Proc. Natl. Acad. Sci. USA 82: 2192-2195.

Stratford, K., A. Mason, A. Larkman, G. Major, and J. J. B. Jack (1989) The modelling ofpyramidal neurones in the visual neurones in the visual cortex. In The computing neuron,Dubin, R., C. Miall, and G. Mitchison, eds., pp. 296- 321, Addison-Wesley, Wokingham,UK.

Traub, R. D. (1982) Simulation of intrinsic bursting in CA3 hippocampal neurons. Neuro-science 7: 1233-1242.

Traub, R. D., F. E. Dudek, R. W. Snow, and W. D. Knowles (1985) Computer simulationsindicate that electrical field effects contribute to the shape of the epileptiform field potential.Neuroscience 15: 947-958.

VI I -124 Appendix

Traub, R. D., F. E. Dudek, C. P. Taylor, and W. D. Knowles (1984) Simulation of in vitrosynchronized hippocampal discharges occuring in the absence of chemical synaptic trans-mission. Abstr. Soc. Neurosci. 10: 548-548.

Traub, R. D., F. E. Dudek, C. P. Taylor, and W. D. Knowles (1985) Simulation ofhippocampal afterdischarges synchronized by electrical interactions. Neuroscience 14: 1033-1038.

Traub, R. D., W. D. Knowles, R. Miles, and R. K. S. Wong (1984) Synchronizedafterdischarges in the hippocampus: simulation studies of the cellular mechanism. Neuro-science 12: 1191-1200.

Traub, R. D., and R. Llinas (1979) Hippocampal pyramidal cells: significance of dendritic ionicconduc tances for neuronal function and epileptogenesis. J. Neurophysiol. 42: 476-496.

Traub, R. D., R. Miles, R. K. S. Wong, L. S. Schulman, and J. H. Schneiderman (1987)Models of synchronized hippocampal bursts in the presence of inhibition. II. Ongoingspontaneous population events. J. Neurophysiol. 58: 752-764.

Traub, R. D., R. Miles, and R. K. S. Wong (1987) Models of synchronized hippocampalbursts in the presence of inhibition. I. Single population events. J. Neurophysiol. 58: 739-751.

Traub, R. D., R. Miles, and R. K. S. Wong (1989) Model of the origin of rhythmic populationoscillations in the hippocampal slice. Science 243: 1319- 1325.

Traub, R. D., and R. K. S. Wong (1983) Synaptic mechanisms underlying interictal spikeinitiation in a hippocampal network. Neurology 33: 257-266.

Traub, R. D., and R. K. S. Wong (1983) Synchronized burst discharge in disinhibitedhippocampal slice. II. Model of cellular mechanism. J. Neurophysiol. 49: 459-471.

Traub, R. D., R. K. S. Wong, R. Miles, and W. D. Knowles (1985) Neuronal interactionsduring epileptic events in vitro. Fed. Proc. 44: 2953-2955.

Other modeling softwareBunow, B., I. Segev, and J. W. Fleshman (1985) Modeling the electrical behavior of anato-

mically complex neurons using a network analysis program: excitable membrane. Biol.Cybern. 53: 41-56.

Carnevale, N. T., and F. J. Lebeda (1987) Numerical analysis of electrotonus in multi-compartmental neuron models. J. Neurosci. Meth. 19: 69-87.

Hines, M. (1989) A program for simulation of nerve equations with branching geometries. Int.J. Biomed. Comput. 24: 55-68.

Wilson, M. A., U. S. Bhalla, J. D. Uhley, and J. M. Bower (1989) GENESIS: a system forsimulating neural networks. In Advances in neural information processing systems,Touretzky, D., ed., pp. 485-492, Morgan Kaufmann, San Mateo, CA.

Appendix VI I -125

VI I -126 Appendix

NODUS 3.2 UPDATE

User InterfaceThe most obvious change is the new style user interface. All dialog windows and popup menusnow use the Geneva 10 font instead of Chicago 12. This change was implemented after asurvey showed that the majority of Nodus 3.1 users would prefer a smaller font size. Theadvantage is that more text can be shown for the same window size. It also looks cool.However, if you don't like the small fonts, there is an option in the Preferences command(Edit menu) which allows you to switch to the old-style System 6 window display. You willhave to restart Nodus before this takes effect. If you use the old-style display, you will noticethat some dialogs have become quite crowded and that some new options have not beenimplemented in the old style dialogs. Also, if you still use System 6, the old-style dialogs willbe shown.The preferences file (Nodus 3.1 manual, p. 29) has been renamed to Nodus_Preferences. Thisfile works only with Nodus 3.2, if you want to continue using Nodus 3.1 you should keep theit's preference file ('Nodus Preferences'). Not only does this new name distinguish if from theold preferences file, but it also allows me to e-mail the preferences file to new users. TheNodus_Preferences' file should be in the same folder as Nodus 3.2 or in the ‘Preferences’folder inside the ‘System Folder’. It is a personalized file, so you should not give it to otherpeople!Scrolling of graphs has been improved. Copy, Cut and Paste (Edit menu) are now available forall text items in dialog and definition windows. Copy of PICT data from a graphics windowremains functional (Nodus 3.1 manual, p. 62).Balloon Help (see your Macintosh manual) has been implemented. All menu commands haveballoons. At present only a few dialog windows have balloons implemented, but in the nearfuture all dialog windows will have balloons. Balloon Help needs the Nodus Help file, whichshould be in the same folder as Nodus. Otherwise, the Help menu will show an Open NodusHelp command which allows you to open the Nodus Help file anyway.

File SystemNodus 3.2 can read all Nodus 3.1 files (and Nodus 2 neuron and conductance definition files).Nodus 3.1 simulation data files can be read, but no simulation window will be created; you haveto do New Simulation and recompile the simulation database (Nodus 3.1 manual, p.41).Nodus 3.2 can save to a new file format. It can save any graph window to a PICT file. PICTfiles are a standard format for graphical data on Macintosh computers and can be Opened bymost drawing, painting and page setting applications. If the active window is a conductance orneuron plot or an old simulation window , the Save As command will change into Save AsPICT File. If you want to save the active simulation plot window as a PICT file, you have topress the option key while selecting the File menu. Nodus 3.2.1 cannot Open PICT files.Another change is the use of ‘smart links’, which uses advanced features of the System 7 filingsystem. As a consequence, linked files (see page 30 of the Nodus 3.1 manual) can now be putanywhere on your hard disk. There is no longer any need to keep linked files together in thesame folder so that they can be Opened together automatically. The Nodus Data folder has beenreorganized; all conductance files have been put together in a folder, as have been all neuron andnetwork files.The System 7 filing system is quite smart. You can move files around to other folders or evenrename them and Nodus will still find the linked files automatically, provided that file names areunique. This is a very important caveat: never have two Nodus files with the same name onyour disk! Also, never Duplicate (or drag and copy) your Nodus files, except for making abackup copy to another disk, because duplicated files will have the same hidden file-ID number(see page 30 of the Nodus 3.1 manual) so that Nodus cannot distinguish them. Note also thatthe Save As command is not the proper way to move a file from one folder to another, instead

Appendix VI I -127

you should Quit Nodus, go to the Finder and drag the file to it's new folder (if use Save Asthe file is given a new hidden file-ID number and considered a new file by Nodus). If you wantto have multiple copies of the same file in different folders (for example a conductancedefinition), you can use the Make Alias command in the Finder (File menu) and put aliases ofthe file in different folders. Like other applications, Nodus does not distinguish aliases from theoriginal file. Finally, note that this 'smart linking' only works for files that have been saved byNodus 3.2, Nodus 3.1 files behave as before.Because of ‘smart linking’, you can put Nodus files in separate folders. I suggest that you donot store your own Nodus files in the Nodus Data folder, but use your own folder. This willmake it easy to update the Nodus Data folder when new example files become available: you canthen drag your old copy of the Nodus Data folder into the Trash and install the new 'NodusData' folder without loosing any of your own files. If you still use System 6, you shouldreorganize the Nodus Data folder. drag all files out of the subfolders (Conductances, etc.) andput them together in one folder.

Conductance DefinitionsNodus 3.2 accepts a much wider choice of conductance equations than Nodus 3.1. First of all,conductances can now depend on voltage, on ionic concentration, or on both. Second, insteadof using a Hodgkin Huxley-type equation, you can tabulate the conductance variables in a textfile. These additions resulted in a major redesign of the conductance definition window (seealso the Nodus 3.1 manual, page 99). In system 7 the definition window shows both theactivation and inactivation equations (if there is no inactivation, only activation is shown), eachin it's own box. At the top left of each box a popup menu allows you to select the type ofconductance equation. Basically three types are supported at present: standard Hodgkin Huxleyequation, a table file containing alpha and beta and a table file containing steady state(in)activation (Minf) and time constant (tau, see Nodus 3.1 manual, p. 20). Each of these typescan be either voltage-dependent, concentration- dependent, or both.If you select the table file option, you have to prepare the table outside of Nodus. A spreadsheetapplication, like Microsoft Excel or Lotus 1,2,3, makes it easy to create these files if you useequations, just save it as a text file. Of course, you can also use experimental data for Minf andtau and use a word processing application to create the table file. In the conductance definitionfile a button appears, which allows you to select the table file with the standard Open file dialog.Table files are read by Nodus when it creates the equation table for a New Simulation (Nodus3.1 manual, p. 69). Because Nodus will usually store the conductance variables for differentvoltages values than the ones listed in the table, interpolation is necessary. As default, Nodususes cubic spline interpolation, but this can be quite slow, especially for large two-dimensionaltables. Therefore, the Preferences command (Edit menu) has a Fast interpolation oftables option. When fast linear interpolation is selected, the equation table will be computedmuch more rapidly, but less accurately. Note also that the maximum errors shown for duringcreation of the equation table apply only to standard Hodgkin Huxley equations, not to table files(because they are usually not smooth functions).

For voltage- or conductancedependent equations, the table file should contain 3columns: the first column is voltage or conductance, the second alpha or Minf and the thirdbeta or tau. Columns can be separated by tabs or spaces. They must be in increasing order ofvoltage (i.e. from hyperpolarized to depolarized) or conductance, but the increase betweenrows must not be constant. This allows you to specify outlying values or to use smaller stepswhen the relation between variable and voltage or conductance is very steep. The first rowcontains a header consisting of 2 integer values, i.e. the number of rows (excluding theheader), followed by the number of dependent variables (usually 2).

VI I -128 Appendix

For mixed voltage- and conductance dependent equations, the table file is two-dimensional, with each row a different voltage and each column a different concentration.Usually two tables are put next to each other, first come all the columns for alpha or Minf,then all columns for beta or tau. The header consists of two rows. The first row of theheader contains 3 integer values, i.e. the number of rows (excluding the header), the numberof concentration values (this is usually the number of columns minus one, divided by two)and the number of dependent variables (usually 2). The second row of the header containsthe concentration values. Note that two-dimensional tables use a lot of memory and may takea lot of time to create. Therefore, Nodus imposes a maximum of 100 columns on thesetables, so you should not use more than 100 concentration values in the table file.

Several examples of table files are supplied in the Nodus Data folder. See the Traub82 andYamada series of conductance definition files.

Finally, you can use two options in the conductance definition window to change the useof time constants for some of the conductance equation types. Conductances determined bysteady state (in)activation and time constant, you can be made instantaneous. This meansthat there is no time constant (if you use a table file, you have only 1 dependent variable, i.e.Minf) and that (in)activation always equals steady state (in)activation. You can also specifythat the (in)activation should have a second time constant. This second time constant(tau2) is not completely independent of the first time constant (tau1, computed by theconductance equation), as tau2 = tau1 * factor, where factor equals the tau2/tau1 which youspecify in the conductance definition window. You also need to specify the fraction of(in)activation gates that uses the second time constant, activation will be computed as: M = (1-fraction)*M1 + fraction*M2, where M1 is activation using tau1 and M2 is activation usingtau2.

Concentration PoolsTo use concentration-dependent conductances, you also must compute concentrations. This hasbeen implemented in Nodus 3.2 as the pool subdefinition, which uses a wide range of modelingfeatures, including diffusion, buffers and Nernst potentials. For more information and adescription of the equations involved see the chapter by Yamada et al. in ‘Methods in NeuronalModeling’ by Koch and Segev (1989).Pools are defined as any other subdefinition with the new Pools command (Neuron menu).Like other subdefinitions, they must be tied to a compartment to become functional, see Nodus3.1 manual p. 33-35 for a description of subdefinitions. However, pool subdefinitions can belinked to each other into groups, which makes them more complex than other subdefinitions.Imagine an onion shell type of model for diffusion, with ten shells. You wouldn’t want to tie allten shells individually to each compartment. Therefore you can group these shells together, bylinking them, and tie the first one of the group to the compartment.

The pool subdefinition dialog window has at the top left a Type popup menu. Poolsubdefinitions are either a pool, which is a volume of unspecified shape, or a shell. Forshells you have to specify the position relative to the membrane of the compartment. Eachgroup can contain one supramembrane shell (i.e. the volume just outside of themembrane) and one submembrane shell (i.e. the volume just inside the membrane), youneed both if you want to use Nernst potentials. If you want to model diffusion, you candefine additional shells, 'inside shells' if you want to model diffusion into the cell, outsideshells if you want diffusion away from the cell. You can use as many inside or outsideshells as you like, up to a maximum of 16 shells in one group. If you use 'inside shells,you probably also want to use the core which is the remaining volume in the center of thecompartment.

Appendix VI I -129

Pool subdefinitions can be linked together in groups. Use the Link to popup menu (topright) to link a ‘child’ pool subdefinition to a ‘parent’ (note that this is relation is opposite tothe way you link compartments in the compartment definition). Each ‘child’ can have onlyone ‘parent’ and vice versa. They are all linked together in a group, named for the first parent(as shown in the Group name at the top right of the dialog window), which can be tied to acompartment. Diffusion is possible only to a ‘parent’ and to a ‘child’.

Each pool has a Size (the thickness of the shell or the volume of the pool) and aMinimum concentration (which is also the initial concentration in µM).

Each pool can have several Actions. If no actions are defined, concentration is constant(this can be useful for a Nernst potential). You select an action with a popup menu, up to 10different actions can be defined for each pool subdefinition. Most action cause theconcentration in the pool to change, except for conductance (in)activation. Each actiontype needs 1 to 3 parameters. Because different types of actions are all listed together, it wasimpossible to show proper titles for all the action parameters. The titles shown reflect the lastaction type that was selected. To show the proper parameter titles for any action, press it’spopup menu or any of it's parameters.

The decay by tau action causes concentration to decay exponentially to the minimumconcentration. The decay rate is determined by a time constant (in ms). This simple methodof modeling changes in concentration is often used when detailed simulation of calcium is notdeemed necessary. See for example the Traub82 Hippocampal Neuron. However, it can alsobe used to implement a simple model of an ion pump. Only one decay by tau action can bespecified for a pool subdefinition.

The diffusion action causes diffusion to a neighboring shell or pool. Diffusion is onlypossible to linked pool subdefinitions (i.e. the ‘parent’ or ‘child’) and is always bidirectional.You have to specify the pool subdefinition to diffuse to, and the diffusion constant (inµm2/ms). For pools a coupling factor must also be supplied, for shells Nodus computes thecoupling factor based on the size of the compartment and the shell thickness.

The ionic current flow action causes the flow through the specified ionic channel tochange the concentration. You have to specify the channel (with a popup menu). Parametersare the fraction of the current that contributes to the concentration (default is 1) and the ionicvalency (e.g. +2 for calcium, +1 for potassium).

The nernst + ionic current action affects the concentration pool in the same way as theionic current flow action, but the ionic current is computed differently. It's reversalpotential is no longer constant (as specified with the Ionic Currents command, Nodus 3.1manual, p. 92), but determined by the Nernst potential. To compute the Nernst potential,both a supramembrane and a submembrane shell must be tied to the compartment (theymay belong to the same group of pool subdefinitions or not), and at least one of them musthave the nernst + ionic current action.

The GHK + ionic current action affects the concentration pool in the same way as theionic current flow action, but the ionic current is computed differently. Ionic current is nolonger a linear function of voltage, but is instead computed by the Goldman-Hodgkin-Katzequation. To use the GHK equation , both a supramembrane and a submembrane shellmust be tied to the compartment (they may belong to the same group of pool subdefinitions ornot), and at least one of them must have the GHK + ionic current action. Notimplemented in Nodus 3.2.1.

The synaptic current flow action causes the flow through the specified synapticchannel to change the concentration. You have to specify the channel (with a popup menu).Parameters are the fraction of the current that contributes to the concentration (default is 1) andthe ionic valency (e.g. +2 for calcium, +1 for potassium).

VI I -130 Appendix

The nernst + synaptic current action affects the concentration pool in the same way asthe synaptic current flow action, but the synaptic current is computed differently. It’sreversal potential is no longer constant (as specified with the Synaptic Currents command,Nodus 3.1 manual, p. 96), but determined by the Nernst potential. To compute the Nernstpotential, both a supramembrane and a submembrane shell must be tied to thecompartment (they may belong to the same group of pool subdefinitions or not), and at leastone of them must have the nernst + synaptic current action.

The GHK + synaptic current action affects the concentration pool in the same way asthe synaptic current flow action, but the synaptic current is computed differently.Synaptic current is no longer a linear function of voltage, but is instead computed by theGoldman-Hodgkin-Katz equation. To use the GHK equation , both a supramembrane anda submembrane shell must be tied to the compartment (they may belong to the same groupof pool subdefinitions or not), and at least one of them must have the GHK + synapticcurrent action. Not implemented in Nodus 3.2.1.

The buffer #1, buffer #2 and buffer #3 actions are identical. They have been namedthis way to allow output of the free buffer concentrations. In the Configure Plots ( Nodus3.1 manual, p. 70) and Text Output (Nodus 3.1 manual, p. 72) commands, the Valuepopup menu includes options to select free buffer #1, etc. These actions implement firstorder buffering of the concentration in the active pool subdefinition. Parameters are the totalbuffer concentration (in µM), the forward rate of buffer binding (in (1/µM•ms) and thebackward rate of buffer binding (in 1/ms). Each of these actions can be used only once in apool subdefinition.

The pump action implements an ion pump. Three parameters are necessary: a Kmax (in1/ms), a Kd (in µM) and a density (in µmol/µm2). See Zador et al., PNAS 87, 6718-6722,1990 for the equation.

The conductance (in)activation action is shown in italic to emphasize that it isdifferent. It means that the active pool subdefinition will be used for the concentration-dependent (in)activation of the selected conductance. The conductance (in)activationaction is available only in pools and submembrane shells. You have to specify theconductance equation with a popup menu (all voltage-dependent conductances are disabled)and the maximum range for the equation table (see Nodus 3.1 manual, p. 20, the table willcontain conductance variables in the range 0 µM to the maximum you specified for the firstconductance (in)activation action encountered during simulation database compilation).

Subdefinition management: is similar to that for other subdefinitions. Additionally, there isa Smart duplicate & link button. This button is useful for duplicating shells if you wantto create an onion model. The duplicated shell will be linked to the original one and have thesame actions (if appropriate).

Neuron DefinitionsApart from pool subdefinitions (see above), other changes are:Current subdefinitions can have up to 13 currents defined.You can have now up to 5 different ties to synaptic current subdefinitions in the compartmentdefinition window (Nodus 3.1 manual, p. 87). This, together with the new Strength field forSynaptic Connections (see next) expands the network capacity considerably.You can also have up to 2 ties to pool subdefinitions.A Variable option has been added to Membrane capacitance in the neuron definitionwindow (Nodus 3.1 manual, p.85). This option is similar to the Variable option forMembrane resistance, e.g. you set the value in the compartment definition window. Notethat either capacitance or resistance can be variable, but not both at the same time. However,one can combine variable membrane capacitance with 2 values for membrane resistance (theFrom compartment # option).

Appendix VI I -131

Network DefinitionsNodus 3.2 can accommodate much larger networks than before: up to 200 neurons, with up to60 connections from each neuron.

A field has been added to the Synaptic Connections (Network menu, Nodus 3.1manual, p. 82) dialog. The Strength field allows you to give specific synaptic connectionsdifferent strengths, default is one. The peak synaptic conductance Gpeak is now:

Gpeak = Gmax * Strength * Transmitter_amountFuture Nodus releases will implement synaptic plasticity by dynamic changes in the

Strength field, Note that one can already model short term potentiation by using apresynaptic calcium pool that controls the amount of transmitter released.

Nodus 3.2 implements gap junctions, which can be both rectifying and non-rectifying. A newcommand Electrical Connections in the Network menu allows you to set up these gapjunctions. The number of electrical connections from each neuron is also listed in the networkdialog window.

The approach is very similar to the one used in the Synaptic Connections command,i.e. you select a neuron from which the electrical connection will start with the from popupmenu at the top. Then you can specify up to 20 connections by selecting a to neuron with apopup menu.

The design of the Electrical Connections dialog differs a bit because a compartmentpreselection popup menu (Nodus 3.1 manual, p. 48) is included for both the from and oneuron. This reflects the fact that these connections go from one compartment to another.You also have to set the conductance G of the connection (in nS). This implements asymmetric, non-rectifying gap junction. To make it rectifying, you first have to define aconductance (New Conductance) that describes the voltage (or concentration) dependence ofthe rectification. You can then select this conductance from the rectification popup menu tomake the electrical connection rectifying. The current flowing through the gap junction iscomputed as:

Ig = Gg * (Vpost - Vpre)Gg is constant (non-rectifying) or a function of Vpre (rectifying).

Nodus 3.2.2 can randomize synaptic weights with the Randomize Strenghts command in theNetwork menu.

Randomize synaptic connection weigths and/or electric connection conductances in anetwork. Randomization can be either as a uniform distribution (specify the range) or asa gauss distribution (specify mean and standard deviation). The new weights will dependon the old ones if the Multiply with existing values option is selected.

IntegrationThe integration routines have changed quite a lot internally compared to Nodus 3.1. As aconsequence, the same simulation may run a bit faster (especially large neurons) or slower(active membrane models in Euler method) and you can expect small differences in the results.In the Euler method (used to be called 'Hybrid Euler'), the changes in variable time step arecontrolled by a different mechanism. This results in slightly different minor time steps (Nodus3.1 manual, p. 25), which may cause different results when large time steps are used. Also, ifyou use a model with pool subdefinitions, you will have to specify both a maximum change involtage and in concentration per minor time step (Nodus 3.1 manual, p. 69) if you use the Eulermethod.In the Fehlberg method, conductances are now also interpolated from the equation table(Nodus 3.1 manual, p. 20), instead of computed by the Hodgkin Huxley equation for each timestep. Usually this will cause no differences, because linear interpolation is correct if the tableuses small voltage steps, but in some active membrane models you might note small differences.

VI I -132 Appendix

The voltage range for the equation table (Nodus 3.1 manual, p. 69) are no longer specific toeach simulation database. Consequently, this range is not shown in the Integration Settingsdialog (Simulation menu) anymore. You can change the voltage range in the Preferencescommand (Edit menu), such a change will apply to all simulations.

Overview Of Changes In Menu Commands

File Menu

New SimulationThe New Simulation command is now always available if a simulation database or neurondefinition file is in memory. In Nodus 3.1 you had to Close an active simulation first beforeyou could do New Simulation. This is no longer true, if a simulation database is still active itwill be automatically closed by the New Simulation command before it displays it’s dialogwindow.

One can now Seed random number generator in the New Simulation dialogwindow.

Open SimulationThe Open Simulation command is now always available In Nodus 3.1 you had to Close anactive simulation first before you could do Open Simulation. This is no longer true, if asimulation database is still active it will be automatically closed by the New Simulationcommand before it displays it’s dialog window.Import NeuronIs no longer a hierarchical menu (Import Simulation has been removed).Two new import formats: NINDS (no coordinates) (is similar to NINDS (withcoordinates), see Nodus 3.1 manual, p. 118, but without the X1, X2, X3, Y1, Y2, Y3fields) and Eutectics™ (the popular reconstruction software, note that this import filter is stillexperimental, if it fails let me know).Save As a neuron definition fileThe Text file option was available in the latest Nodus 3.1 versions, but is not described in theNodus 3.1 manual.

One can save the neuron definition file to a text file with all the connections betweencompartments listed (Text with links, this is similar to the printed output) or without listingthe connections (Text without links). I both cases all compartment data are separated byTABs, so the file can be easily imported into a spreadsheet or statistics application. For textfiles Nodus uses the following numbers to describe the type of connection (link) betweencompartments:

1: a node connection (parent to child)2: a branch connection (parent to child)3: a node connection (child to parent)4: a branch connection (child to parent)

Save As PICT fileVariant of Save As command. See section on File System.Revert:New command. Deletes all changes you made to a definition and reloads it from the file. Thiscommand is available only after you have made changes to the active definition and if thosechanges have been stored in memory.Print a neuron definition fileThe complete printing of neuron definition files was available in the latest Nodus 3.1 versions,but is not described in the Nodus 3.1 manual.

Appendix VI I -133

The complete neuron definition file contents are printed. This includes the cableparameters and comment; the name, shape, size, electrotonic length, local membranecapacitance or resistance, coordinates (if present), ties to subdefinitions (numbered) and linksto other compartments of all compartments; and all subdefinitions. While printing neurondefinition files Nodus uses the following symbols to describe the type of connection (link)between compartments:

-o- a node connection (parent to child or vice versa)|-> a branch connection (parent to child)|<- a branch connection (child to parent)

Edit Menu

CopyThe Copy command works for text, see section on User Interface.CutThe Cut command works for text, see section on User Interface.PasteThe Paste command works for text, see section on User Interface.DuplicateWhen active, this command will show either Duplicate Neuron, Duplicate Compartmentor Duplicate Conductance, depending on what type of definition window is active.Duplicate Neuron makes a copy of the active neuron definition in memory and shows itsdefinition window, it is very similar to doing a Save As of a neuron definition (Nodus 3.1manual, p. 30-31), except that no new file is created till you Save the copy.Duplicate Compartment is a much more useful command. It makes a copy of the activecompartment and inserts it behind the active compartment (or appends it). This is very usefulwhen you are creating a neuron model that has a lot of similar sized compartments, as the newcompartment will have the same size and attributes as the active compartment. Branches linkedto the active compartment are not copied. If the next compartment (by number) had not beendefined yet, than Duplicate Compartment overwrites that compartment, otherwise the newcompartment is inserted and the number of compartments increased by one.Duplicate Conductance makes a copy of the active conductance definition in memory andshows its definition window, it is very similar to doing a Save As of a conductance definition(Nodus 3.1 manual, p. 30-31), except that no new file is created till you Save the copy.PreferencesSeveral new options have been added to the Preferences command.

System 6 (large fonts): see section on User Interface.Fast interpolation of table: see section on Conductance Definitions.Equ. table from: see section on Integration.

Simulation Menu

Start/Pause/ContinueThe Run command (Nodus 3.1 manual, p. 68) has been renamed. The command is now calledStart if the simulation has not been started yet and Continue after a Pause. The functionalityhas not been changed.Pause AtNew command. Let’s you specify a time at which the simulation will pause. You can thenchange experiment or Text Output settings and Continue.Integration methodThe 'Equation table from' has been moved to the Preferences command, see section onIntegration.

VI I -134 Appendix

Configure Plots:A Legend option has been added. It prints the variable type (in black) and names (in

color) in the Simulation Plot window.You can now specify up to 6 plots for each axis.

Text Output:The 'Make legend' option has been renamed Legend. It outputs 4 rows names,

corresponding to the names of the value type (first row), followed by the neuron name,compartment name and subdefinition name. Legend is enabled only when Separated byTABs option is active.

You can now specify up to 6 values for each value type.

Current Clamp:This command is disabled if a Voltage Clamp is active in a single neuron simulation (and viceversa).

'Cyclical' has been renamed Repetitive, functionality has not changed. White noisecurrents (Nodus 3.1 manual, p. 79) have now a gaussian distribution, with mean zero.Instead of an Amplitude you have to specify the standard deviation Stand. Dev. and the'Seed' is no longer specified, but you can set the seed for the random number generator in theNew Simulation command.

File input for Current Clamp. A disk icon has been added to the Current Clampcommand. It allows you to specify a file from which current injection values will be taken.You also can specify an index into the file (if it contains more than 2 columns).

The file should have the format:

time0 current1.0 current2.0 ...

time1 current1.1 current2.1 ...

...

timen current1.n current2.n

^ ^

index 1 index 2Columns can be separated by spaces or tabs. You can use any time interval or starting

time. Current will be zero before time0 and equal current#.n after timen (as long time isbefore the End specified in the Current Clamp dialog). No interpolation is performed.

Voltage ClampThis command is disabled if a Current Clamp is active in a single neuron simulation (and viceversa).Synaptic Firing Times:A Random firing at option has been added. Click on this option and a edit field will appearin which you can specify the mean firing frequency (in Hz) for the selected synapse. Synapsewill fire using a Poisson distribution with the specified mean. The random number generatorcan be seeded in the New Simulation command.Network Menu

Synaptic ConnectionsA Strength field has been added, see section on Network Definitions.

The Delete all from this neuron button deletes all synaptic connections for which thecurrent from neuron is the presynaptic neuron.

Electrical ConnectionsNew command. See section on Network Definitions.

Appendix VI I -135

Randomize StrenghtsNew command. See section on Network Definitions.Neuron Menu

Go to CompartmentThe dialog has now also preselection and compartment popup menus (Nodus 3.1 manual,

p. 28) that allow you to select the compartment. These popup menus become inactive if youtype in a number.

Sholl PlotThis command was available in the latest Nodus 3.1 versions, but is not described in the Nodus3.1 manual.Sholl Plot draws a neuron diagram of the active neuron definition file in a graphics window withscroll bars. The command is available only for neuron definitions with the Tree modelformat option active. One can Copy, Save As PICT or Print the neuron diagram. Thelayout of the Sholl plot is under user control.The dialog window has the following options:

Layout: either a Horizontal (soma at left) or Vertical (soma at bottom) Sholl plot isdrawn.

Lengths: the morphological, Real length (in µm) of the compartments is shown or theirElectrotonic length. You also have the option of loading values From a text file (asingle column, one value for each compartment).

Diameters: are Not shown (compartments are drawn as line segments, this is thestandard Sholl plot) or the morphological, Real diameters are shown (compartments aredrawn as rectangles). Spherical compartments are always shown as circles.

Expand weight factors: if enabled any branch connected with a weight factor of n,larger than one, will be drawn n times. If not enabled all compartments are drawn only once.Branch connections are always drawn differently from node connections: the branchcompartment is connected to the center of the parent compartment.

Scale: the size of the Sholl plot is determined by this setting. For Real Lengths theunits are pixels/µm (a pixel is 1 point on the screen, most Mac screens have a resolution of 72pixels per inch), for Electrotonic Lengths the units are pixels/millilamba (i.e. per 0.001lamba). Because of minimum spacing between branches requirements, the Sholl plot willalways have a minimum vertical (Horizontal Layout) size. If the Sholl plot is much largerthan your screen, it might help to Copy it and Paste it in a graphics application window (likeMacDraw) that allows you to zoom out.

Transmitter ReleaseTransmitter release can now be Concentration pool dependent. You also have to specify ascaling Factor and a Power (e.g. it is often assumed that transmitter release is proportional tothe third power of the calcium concentration).Synaptic CurrentsThe implementation of conductance dependent synapses has been modified. Conductancedependence is now an extra property that a synapse can have. The time course of the synapticcurrent is determined by the Alpha or Dual exponential function, but if Also conductancedependent is selected, it's peak conductance will be determined by the conductance equation.An example of a NMDA synapse is included (neuron definition file Spine with NMDA channel).PoolsNew command. See section on Concentration Pools

Conductance MenuConductance Plot windows are now recognized as belonging to a conductance definition andactivate the Conductance menus.

VI I -136 Appendix

Plot Scales'Potential range' has been renamed Variable range and a 0 to ... µM field has been added.This controls the range for concentration dependent conductances. For conductances that areboth potential and concentration dependent, both variables change together; you probably wantto constrain one variable by giving it a very narrow window.The Overlay existing plots button is now active. Note that this option must have beenselected before you draw the original plot.

Appendix VI I -137

Nodus ftp siteInstructions for ftp:Most of the files at the ftp site have been compressed and binhexed with StuffIt 1.5. The filesare marked by a double suffix: '.sit' for the compression and '.hqx' for the binhex conversion.Some ftp software (like Fetch) automatically decodes and decompresses the files afterdownloading them, I strongly advise you to use such software. Otherwise, download the files,and use either StuffIt 1.5, StuffIt Lite, StuffIt Deluxe or another compatible application todecompress these files. First Decode BinHex File the file (this strips the '.hqx' suffix) andthen Open Archive the file, select the content and press the Extract button.You can also consult our www page at http://bbf-www.uia.ac.be/

Using the ftp siteThe Nodus server is a unix machine called 'bbf-ftp.uia.ac.be' (143.169.8.193). You can accessit by standard anonymous ftp. Detailed instructions for ftp from another unix machine:

yourhost% "ftp bbf-ftp.uia.ac.be"Connected to 143.169.8.193.220 kuifje FTP server (SunOS 4.1) ready.

Name (143.169.8.193:yourname): "anonymous"331 Guest login ok, send ident as password.

Password: "[email protected]"230 Guest login ok, access restrictions apply.

ftp> "cd nodus"250 CWD command successful.

ftp> "ls"200 PORT command successful.150 ASCII data connection for /bin/ls (131.215.137.243,1682)(0 bytes).NODUSINFOTEXTNodus3.2.2.sit.hqxNodus3.2.2Q.sit.hqxNodusData.sit.hqxNodusHelp.sit.hqx

NodusInfoWord.sit.hqxREADMEdemo_version_only226 ASCII Transfer complete.144 bytes received in 0.02 seconds (6.1 Kbytes/s)

ftp> "binary"200 Type set to I.

ftp> "mget Nodus*"mget Nodus3.2.1.sit.hqx? "y"

200 PORT command successful.150 Binary data connection for Nodus3.2.1.sit.hqx (131.215.137.243,1690)

(712750 bytes).226 Binary Transfer complete.

local: Nodus3.2.2.sit.hqx remote: Nodus3.2.1.sit.hqx 712750 bytes received in 5.8e+02 seconds (1.2 Kbytes/s) mget Nodus3.2.2Q.sit.hqx? "n" or "y", your choice

. . .ftp> "quit"

221 Goodbye.

VII I -138 Index

VIII. INDEX

1 time axis range 713-dim coordinates 39, 59, 84, 87, 89, 9050% Reduction 61, 63About Nodus 55accuracy 89- 91activationactivation factor 20, 21, 26, 37, 44-46, 99-

101all comps 50-51Allow editing 70alpha function 23-24, 97amplitude 75Apple Menu 55Automatic compartment names 59Automatic loading/saving 29, 63Automatic Saving 29, 62, 63backup 35background 7, 63base amount 22, 94Beep when finished 63Before closing definition file 64Before closing simulation file 64binary branch 17block 79Block Ionic Currents 48, 79branch connection 16-19, 39, 64, 89-91,

106, 111branch connection icon 87cable parameters 11-14, 36, 83, 84, 91camera lucida 13channel 20-21, 99characteristic potential 22check mark 28Clear 62Clear all synaptic events 46, 57Clear experiments & text output 44, 57Close 42, 60, 116Close All 60, 116Close All Graphs 60Close simulation 42, 65, 116CM 11Comment 37, 40compartment 10-15, 19, 33, 37-39, 49, 82-

88, 91compartment definition window 33, 84-88,

106compartment labels 38compartment name 50, 59, 64, 84compartment number 37, 49, 86, 84- 89compartment preselection popup menu 49,

50-51, 64, 77

compartment selection popup menu 49, 50,64, 75, 77, 82, 85

compartment shape 85compartment size 84, 89-91compartmental model 11, 12compilation 44Compile from 43computation speed 14, 25, 46-47, 65, 90-

91, 94concentration 26, 48conductance 20, 23, 79, 87, 97, 101conductance blocking factor 49conductance definition file 29, 32, 35-37,

58-59, 92, 97, 115conductance definition window 38, 99-100,

107conductance equation 99-100Conductance menu 99-102Conductance Plot window 60, 62, 99-101,

107conductance popup menu 97Configure Plots 41, 48, 64, 66, 70-71, 104connection 16-17, 24, 86-87constant transmitter release 24, 95coordinate 84, 87Copy 62Copyright 2cross connection 17crosshair cursor 65-66CS A Current 110, 113CS Delayed Rectifier 113CS Fast Na Current 113Current Clamp 48, 74-76, 105current display 75current injection 26, 75Current plots 63Cut 62cyclical 75cylinder 11-13, 85cytoplasmic resistance 11, 15database compilation 43Default to resting potential 63Default to set potential 63Default to values in memory 63definition window 61, 64delay time 24, 82Delete all currents 74Delete all firing times 78Delete voltage clamps 76dendrite 37diameter 11, 18-19, 89-90differential equation 12

Index VII I -139

double precision 24, 36Double size simulation plots 61, 63Draw axes 71dual exponential function 23-24, 97Duplicate 35, 93, 96, 98dynamic equilibrium 44, 46E 11Edit axis 70Edit Menu 62-64electronic circuit 11, 24electrotonic length 13-14, 47, 86, 90-91empty selection 49equation 36, 38equilibrium 44equivalent circuit 12, 15, 21, 24equivalent cylinder 15error message 51-53estimated error 66Euler method 24-25, 47excitable compartment 33excitable membrane 9, 20, 33experiment 50experiment commands 41, 48, 74-79experimental data 36-37experiments 47, 57, 74-79exponential peeling method 15factor 22Fehlberg method 25, 47, 66, 69File format 59, 61file hierarchy 30, 37file ID-number 30file links 30File menu 28, 55-61file name 30, 33, 38file structure 28, 30, 37Finder 7, 30firing 24fixed time step 69font 63foreground 46fuse 89, 91Fuse Compartments 37, 40, 88-89, 91gate 20H 20, 99Genesis 61, 118Gmax 21, 23, 64, 93Go to Compartment 88, 116graphics window 61-63, 100grow box 65hardware 7, 46heap memory 7, 55HH Delayed Rectifier 112HH Fast Na Current 112Hide 60Hide All 60

High multifinder priority 46-47, 63, 105Hodgkin Huxley equation 20, -2322hybrid method 25, 47, 66, 69ID-number 30, 61import format 37, 118-119Import Neuron 37, 38, 59, 61, 85, 118-119Import Simulation 59in mS/cm2 64inactivation 20-21, 37, 101information 2inheritance 44initial values 44, 57, 63injected current 49, 74-76injection list 75input resistance 13, 14integration commands 42integration error 26integration method 24, 25, 68-70Integration Settings 42, 66, 68-70, 104integration speed 14, 25, 46-47, 65, 90-91,

94invertebrate 17, 106, 120ion concentration 10ionic currents 10, 21, 33-38, 64, 87, 90, 92-

94, 106Ionic Currents… 92-94italicized 28, 42, 67Keep 3-dimensional coordinates 59Kill 60λ 12Ladder 61, 118Leak 93leak conductance 21, 93length 11, 19, 89- 90linear cable theory 11link 30, 32, 81, 86local name 49, 81lump 15M 20, 99Macintosh 5, 7, 46major time step 25- 26, 47, 69, 73, 75, 79Make legend 73mathematics 9maximum 115Maximum […] 69maximum conductance 21, 23, 93, 97maximum error 25Maximum ∆V 66, 69Measure Window 28, 65, 66, 79, 80measuring 65, 66, 80membrane capacitance 11, 14, 86membrane potential 26membrane resistance 11, 14, 86membrane surface 13-14, 19membrane voltage 94

VII I -140 Index

memory 7, 30, 43, 48, 55, 115menu bar 27minor time step 25-26, 68, 69model 36, 41, 43, 51model parameters 9, 14morphology 10, 13, 14, 37, 40MultiFinder 7, 46Multiple use of subdefinitions 35, 64, 93,

95, 98network 24, 36network definition file 29, 32, 40, 58, 80,

115network definition window 40, 80-81Network menu 80-82Neuron 36neuron definition file 29, 32-35, 39, 40, 58-

59, 83, 106, 108, 115neuron definition window 83-84, 87, 88Neuron Diagram 60, 62Neuron menu 28, 83-98, 116neuron model 34, 37, 91neuron selection popup menu 49, 77, 81, 82New 56-57New Conductance 37, 57New Network 40, 57New Neuron 38, 57New Simulation 41, 43-46, 51, 53, 56-57,

63, 116Next Compartment 39, 88, 106Next value type 73NINCDS 118node connection 16-19, 87-89, 91, 106, 111Nodus 2 5, 7, 35Nodus Preferences 7, 29, 63Nodus Resume file 29, 62not used 49nS 64Number of compartments 39, 47, 83Only node connections 64Open 32, 35, 57-59, 116Open all linked files 33, 58Open Conductance 58Open Network 58, 109Open Neuron 58, 106, 108Open Simulation 41, 42, 44, 45, 46, 57Optimize Model 37, 39, 40, 87, 90-91option key 28, 116original model 40output 29, 48, 49Output at major time step 73Output maxima/minima 73Oxford 119Page Setup 61paper 2, 120parameter selection 51-53, 70-71, 74, 77-79

parameter selection popup menu 41, 48-50parameter selection popup row 48-50parameters 37, 88parent branch 18, 19parent compartment 17, 86-87, 106passive compartment 11, 33passive membrane 9, 13-14, 87passive membrane model 14Paste 62Pause 42, 68period 76Plot (In)Activation 38, 101plot buffer 72Plot Conductance 100Plot Rate Factors 101Plot Scales 100-101Plot Time Constants 101Plot Window 28, 57, 60, 62, 64-65, 72, 79,

80popup menus 28, 49popup row 48-50, 70-71, 74, 77-79postsynaptic compartment 33postsynaptic conductance 23-24postsynaptic current 23-24postsynaptic neuron 82postsynaptic site 23-24, 34, 36, 87potential threshold 94Preferences 35, 41, 44, 46, 47, 50, 60, 61,

62-64preferences file 7, 29presynaptic 22, 24presynaptic compartment 33presynaptic neuron 82presynaptic potential 22, 24presynaptic site 24, 35-36, 87Previous Compartment 88Previous value type 73Print 61, 63Print font 61, 63process 22-23, 24Quit 62Quit when finished 63ramp current 75range preselection popup menu 51rate factor 20, 99, 101raw parameter selection numbers 52reduced model 14, 15-19Reference 2, 120relative error 26, 66, 70Repetitive sweeps 71, 76Reset simulation numbers 63resting membrane potential 12Resting potential 46RI 11RM 11

Index VII I -141

RN 13Run 41, 42, 64, 68, 104run cursor 66, 68Runge-Kutta method 24, 25Save 30, 35, 61Save As 30, 32-33, 35, 61Scale Sizes 13, 87, 91-92scaling factor 13, 84scroll bar 65Select by structure type 50-51, 64Select only named comparts 59selection of simulation parameters 38selection popup row 48-50, 52, 64Separated by TABs 73Set Connections 40SF 13shift key 28, 88, 116Show all compartments 75Show only named comparts 50, 64, 85shrinkage 13, 14, 91shut off time 47, 69side-branch 17-19, 111simulation data file 29, 32-33, 36, 44-45,

57, 103, 115simulation database 29, 36, 41-43, 48, 51,

53, 56, 62, 64, 70, 103, 116simulation database windows 28simulation length 69, 78Simulation menu 28, 41, 42, 66-79, 104simulation number 41, 63, 103simulation parameters 44, 47-49, 56, 70, 81,

85simulation plot window 64, 103simulation run file 36simulation speed 14, 25, 46-47, 65, 90-91,

94simulation time 68single precision 36singular point 20sinusoidal current 75software 5, 122soma 37source 43source model definition file 32, 41, 43, 48,

52-53, 58space constant 12sphere 11, 13, 85split 90, 91Split Compartment 37, 40, 87, 89-90, 91spreadsheet 29, 37Squid Giant Axon 112Squid Giant Axon Demo 112state 99Status Window 28, 65, 80steady current 75

steady state conductance 100step 76stiffness 25-26, 46-47Structure type 38, 39, 50, 85structure type preselection popup menu 50,

64subdefinition 34, 35, 38-39, 64, 84, 90, 92-

98, 106subdefinition dialog window 34subdefinition popup menu 49, 87subdefinition selection 33subdefinition selection popup menu 34, 92-

98subdefinitions 33, 87, 89submenu 27, 117synapse 22, 47synaptic conductance 26synaptic current 34, 82, 90, 98Synaptic Currents 33, 34, 38, 64, 87, 96-

98, 108synaptic event 46synaptic firing time 24, 79Synaptic Firing Times 48, 50, 78-79synaptic shut off time 47, 69synaptic switch off time 24System 7, 46table 25, 47, 69template 43Test 7 Network 109Test 7 Network Demo 108test model 38Test-cell 1 110Test-cell 1 Clamp 110Test-cell 2 106Test-cell 2 Demo 103Test-cell 3 110Test-cell 4 111Test-cell 5 111Test-cell 6 108Test-cell 6 Demo 1 107Test-cell 7 109text attributes 5text file 29, 59, 61, 72-73Text Output 41, 48, 66, 69, 72-74text output file 29, 72Threshold 73threshold potential 22, 94tie 89, 106Tile windows 63time constant 12, 14, 86, 101time sharing 46time to peak 97Time Window 28, 65, 80, 103τm 12transmitter amount 79, 94

VII I -142 Index

transmitter release 22, 24, 33, 35, 87, 94-96Transmitter Release 38, 94-96transmitter release site 82tree format 17, 39, 85tree model option 85, 87triangular current, 75Trigger all output 73tuning 39, 40type 1 synapse 23-24, 78, 94, 95type 2 synapse 23-24, 95, 97Undo 62unit popup menu 71, 74Update all links 30, 61Update all links in memory 61Update Plots 71Value 70value type selection popup menu 48, 67Values in memory 45variable membrane resistance 15variable time step 24, 69variable transmitter release 24, 26, 95vertebrate 17, 121View/Edit Parameters 46, 48, 66, 70virtual memory 46voltage clamp 22, 26, 38, 48, 49, 63, 71,

76-78voltage clamp cycle 76voltage clamp length 76Voltage Clamp… 76-78voltage dependent 20voltage range 47Warnings 60, 62, 64warranty 2weight factor 16-17, 19, 39, 64, 87, 106white noise current 75window commands 42∂V/∂t 70

Documents

Nodus 3 - UAntwerpen · The ‘Nodus Preferences’ file should be placed together with the Nodus 3.2 application file (i.e. both on the desktop or both in the same folder) or it