Bionics Chemical Synapse - Imperial College London · PDF fileBionics Chemical Synapse by Surachoke Thanapitak December 2011 A thesis submitted for the degree of Doctor of Philosophy

Bionics Chemical Synapse

by

Surachoke Thanapitak

December 2011

A thesis submitted forthe degree of Doctor of Philosophy of Imperial College London

Department of Electrical and Electronic EngineeringImperial College of Science, Technology and Medicine

Acknowledgements

First of all, I would like to express my gratitude to Professor Chris Toumazou who has

been my supervisor for the past five years, ever since I was an MSc student in 2006.

Without his support and encouragement, this work would not have reached a successful

conclusion. Professor Toumazou not only inspired my interest in analogue circuit design

but he has also enlightened me to understand how important it is especially in the field

of bionics.

Secondly, I am grateful to my fellow researchers at the Centre of Bio-inspired Technol-

ogy and other groups, including Dr. Panavy Pookaiyaudom, Dr. Thanut Tosanguan,

Jakgrarath Leenutaphong, Yan Liu, Abdul Al-ahdal, Achirapa Bandhaya, Jackravut

Dejvises, Supattra Visessri, Soratos Tantideeravit, Sasinee Bunyarataphan and Parinya

Seelanan. Also, I would like to thanks Dr. Timothy Constandinou, Dr. Pantelis Geor-

giou, Dr. Amir Eftekhar and Dr. Themistoklis Prodromakis for all of their helpful

advice and support through out the period of my PhD studentship.

The Royal Thai Government is an organisation which I feel deeply indebted for their

support throughout my study in the UK. Without the financial support from the Royal

Thai Government, I would not have been able to study at Imperial College. Also,

I would like to thank the Office of Educational Affairs (OEA) for looking after me

throughout my stay in London.

Finally, this work would not have been completed without the love and kind support

from my family back home in Chiang Mai, Thailand.

i

To the king, country and my parents.

ii

”No matter how much you think, you won’t know.Only when you stop thinking will you know.But still, you have to depend on thinking so as to know.”

From ”Gifts He Left Behind: The Dhamma Legacy of Ajaan Dune Atulo”, compiled by Phra Bodhinandamuni,translated from the Thai by Thanissaro Bhikkhu. Access to Insight, 16 June 2011,http://www.accesstoinsight.org/lib/thai/dune/giftsheleft.html .Retrieved on 7 November 2011.

iii

Abstract

This thesis presents the very first bionics chemical synapse which has the capability to sense

the neurotransmitter (glutamate) and imitates the physiological behaviour of certain chemical

synapse receptors (i.e. AMPA, NMDA, GABAA and GABAB). This bionics chemical synapse

consists of two main parts: the glutamate ISFETs that act as neurotransmitter sensors and the

current-mode CMOS circuits that have been designed to match the physiological behaviour of

the chemical synapses.

This bionics chemical synapse requires a sub-nano Siemens operational transconductance am-

plifier (OTA) to develop a low conductance gain for each chemical synapse receptor (0.1nS). A

combination of two OTA designs was required to decrease the overall transconductance gain,

which were: the bulk driven transistor and the drain current normalisation.

To create the bionics chemical synapse, a neurotransmitter sensor is required as the chemical

front-end for each receptor circuit. The sensor that was used is an enzyme-modified ISFET with

glutamate oxidase immobilisation, to make the ISFET sensitive to glutamate ions. Additionally,

a fast chemical perturbation technique called iontophoresis was applied to generate the gluta-

mate stimulus, which represents the neurotransmitter signal. This signal has a one millisecond

time duration.

Finally, the current-mode CMOS circuits biased in the weak inversion region have been de-

signed to match a biological model of the four mentioned chemical synapse receptors. Circuit

techniques, such as the log domain filter and the translinear loop, were applied to realise the

complex mathematical functions in the chemical synapse model. The measured response of the

fabricated AMPA and NMDA receptors, where the glutamate ISFET was used to sensed the

artificial neurotransmitter stimulus, closely matches with the circuit simulation results.

iv

Abbreviations and Acronyms

AMPA Alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid

BJT Bipolar junction transistor

CNS Central nervous system

CMOS Complementary metal oxide semiconductor

CNBH Cynaoborohydride

EnFET Enzyme field effect transistor

GABA Gamma-aminobutyric acid

GluOX Glutamate oxidase

ISFET Ion sensitive field effect transistor

KCL Kirchhoff’s circuit laws

LPeD1 Left pedal dorsal 1

MOSFET Metal oxide field effect transistor

NMDA N-Methyl-D-aspartic acid

OTA Operational transconductance amplifier

PECVD Plasma enhanced chemical vapour

PBS Phosphate Buffer Saline

PLL Poly-l-lysine

REFET Reference field effect transistor

SCI Spinal cord injury

VD4 Visceral dorsal 4

VLSI Very large scale integration

vi

Contents

Acknowledgements i

Abstract iv

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 Silicon Neuromorphic . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.2 Low-gain OTA design . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3.3 ISFET and Iontophoresis Technique . . . . . . . . . . . . . . . . 5

1.3.4 Bio-inspired Chemical Synapse . . . . . . . . . . . . . . . . . . . 6

2 Silicon Neuromorphic 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Neuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

vii

CONTENTS viii

2.2.1 Ion channels and electrical properties of membranes . . . . . . . 11

2.2.2 Nernst Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Action potential and its model . . . . . . . . . . . . . . . . . . . . . . . 13

2.3.1 Generation of Action Potential . . . . . . . . . . . . . . . . . . . 13

2.3.2 Membrane potential model . . . . . . . . . . . . . . . . . . . . . 14

2.3.3 Hodgkin and Huxley model . . . . . . . . . . . . . . . . . . . . . 17

2.3.4 Single Compartment . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4 The Synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.4.1 Electrical Synapse . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.4.2 Chemical Synapse . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.4.3 Biological model for the chemical synapse . . . . . . . . . . . . . 30

2.4.4 Postsynaptic simulation . . . . . . . . . . . . . . . . . . . . . . . 36

2.5 Silicon neuromorphic circuits . . . . . . . . . . . . . . . . . . . . . . . . 42

2.5.1 Silicon Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.5.2 Silicon Synapses . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3 Low-gain OTA design 53

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.2 Analysis of differential pair as an OTA . . . . . . . . . . . . . . . . . . . 54

3.3 Analysis with signal flow graph technique . . . . . . . . . . . . . . . . . 56

CONTENTS ix

3.4 Analysis of a bulk driven OTA with source degeneration and bump lin-

earisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.5 Analysis of double differential pair OTA . . . . . . . . . . . . . . . . . . 62

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4 ISFET and Iontophoresis Technique 72

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.2 ISFET principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.2.1 ISFET Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.2.2 ISFET sensitiviy . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.2.3 Reference electrode . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.2.4 Drift in ISFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.3 Enzyme-Immobilised ISFET . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.3.1 Glutamate ISFET . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.4 Coulometric titration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

4.5 Iontophoresis method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

4.6 Experimental results on Iontophoresis . . . . . . . . . . . . . . . . . . . 99

4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5 Bio-inspired Chemical Synapse 110

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.2 Neural bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

CONTENTS x

5.2.1 Non-invasive neuron stimulus . . . . . . . . . . . . . . . . . . . . 113

5.2.2 Hippocampal neural bridge . . . . . . . . . . . . . . . . . . . . . 113

5.3 Implementation of chemical synapse receptor . . . . . . . . . . . . . . . 115

5.3.1 AMPA receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.3.2 NMDA receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5.3.3 GABAA receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.3.4 GABAB receptor . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

5.4 Implementation of the postsynaptic transmission . . . . . . . . . . . . . 124

5.4.1 Postsynaptic circuit for the AMPA receptor . . . . . . . . . . . 126

5.4.2 Postsynaptic circuit for the NMDA receptor . . . . . . . . . . . 129

5.4.3 Postsynaptic circuit for the GABAA receptor . . . . . . . . . . . 134

5.4.4 Postsynaptic circuit for the GABAB receptor . . . . . . . . . . . 136

5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

6 Conclusion and Future Work 146

6.1 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

6.2 Recommendation for Future Work . . . . . . . . . . . . . . . . . . . . . 149

6.2.1 Integration of the components on the same chip . . . . . . . . . . 149

6.2.2 The non-invasive and direct extracellular glutamate detector . . 150

6.2.3 Live neuron experiment . . . . . . . . . . . . . . . . . . . . . . . 150

A Publications 154

B PCB outline of Bionics Chemical Synapse 155

xi

List of Tables

2.1 Hodgkin and Huxley nerve axon model parameters . . . . . . . . . . . . 23

2.2 Transformed Hodgkin and Huxley axon model parameters . . . . . . . . 23

2.3 Summary Properties of Synapses . . . . . . . . . . . . . . . . . . . . . . 31

3.1 Important parameters of each OTAs design . . . . . . . . . . . . . . . . 69

4.1 Common analytes and immobilised enzymes used in EnFET . . . . . . . 84

4.2 Data of the measured results for different HCl concentration of 0.5, 1,

1.5, 2 and 2.5mM from a voltage-mode readout circuit [30] . . . . . . . . 86

4.3 Data of the measured results for different HCl concentration of 0.5, 1,

1.5, 2 and 2.5mM from the current mode readout circuit in [31] when

Vref = 0.44V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.4 Data of the measured results for different glutamate concentration of 0.5,

1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when

Vref = 0.26V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.5 Data of the measured results for different glutamate concentration of 0.5,

1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when

Vref = 0.21V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

xii

5.1 AMPA, NMDA, GABAA and GABAB parameters . . . . . . . . . . . 141

xiii

List of Figures

1.1 Chemical synapse (a) and Electrical circuit implement (b) . . . . . . . . 4

2.1 The structure of a neuron . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Typical Nerve Action Potential . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Equivalent electrical circuit for the Hodgkin-Huxley model . . . . . . . . 18

2.4 Rate constants (a) αn and (b) βn . . . . . . . . . . . . . . . . . . . . . . 24

2.5 Activation of potassium channel (n) . . . . . . . . . . . . . . . . . . . . 25

2.6 Rate constants (a) αm and (b) βm . . . . . . . . . . . . . . . . . . . . . 25

2.7 Activation of the sodium channel (m) . . . . . . . . . . . . . . . . . . . 26

2.8 Rate constant (a) αh and (b) βh . . . . . . . . . . . . . . . . . . . . . . 26

2.9 Inactivation of the sodium channel (h) . . . . . . . . . . . . . . . . . . . 27

2.10 (a) The action potential (vm) observed when applied with (b) the total

membrane current (Im) . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.11 Electrical synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.12 Chemical synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

xiv

LIST OF FIGURES xv

2.13 MATLAB simulation of rAMPA . . . . . . . . . . . . . . . . . . . . . . . 33

2.14 MATLAB simulation of rNMDA . . . . . . . . . . . . . . . . . . . . . . . 34

2.15 MATLAB simulation of rGABAA . . . . . . . . . . . . . . . . . . . . . . 35

2.16 MATLAB simulation of rGABAB . . . . . . . . . . . . . . . . . . . . . . 36

2.17 (a) Single spike of AMPA neurotransmitter and (b) its postsynaptic

response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.18 (a) Four spikes of AMPA neurotransmitter and (b) its postsynaptic re-

sponse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.19 (a) Single spike of NMDA neurotransmitter and (b) its postsynaptic

response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.20 (a) Four spikes of NMDA neurotransmitter and (b) its postsynaptic

response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.21 (a) A single spike of GABAA neurotransmitter and (b) its postsynaptic

response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.22 (a) Four spikes of GABAA neurotransmitter and (b) its postsynaptic

response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.23 (a) Ten spikes of GABAB receptor and (b) its postsynaptic response . . 41

2.24 Hodgkin and Huxley implementation on CMOS of Toumazou et al. [29] 43

2.25 r implementation with a Bernoulli cell by Lazaridis et al. [33] . . . . . . 45

2.26 Gordon’s synapse circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.1 A differential pair transconductance amplifier . . . . . . . . . . . . . . . 54

LIST OF FIGURES xvi

3.2 (a) A transistor with corresponding voltages and currents. (b) The small

signal equivalent circuit for the bulk transistor. (c) The signal flow graph

of dimensionless model for the bulk transistor. . . . . . . . . . . . . . . 57

3.3 Differential pair as an OTA with double source degeneration . . . . . . . 58

3.4 (a) The half equivalent circuit of OTA in Fig.(3.3). (b) The signal flow

graph of Fig.(3.4(a)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.5 Simulation result for transconductance amplifier in Fig.(3.3) . . . . . . . 59

3.6 Bulk differential pair as an OTA with double source degeneration . . . . 60


graph of Fig.(3.7(a)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.8 Simulation result between output current and differential input voltage

of the OTA in Fig.(3.6) . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.9 Variable linear range OTA of S.P. DeWeerth et al. . . . . . . . . . . . . 64

3.10 Half circuit of the inner differential pair of Fig.(3.9) . . . . . . . . . . . 65

3.11 The double differential pair OTA . . . . . . . . . . . . . . . . . . . . . . 66


graph of Fig.(3.12(a)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.13 Simulation result between output current and differential input voltage

of the OTA in Fig.(3.11) . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.1 Ion Sensitive Field Effect Transistor . . . . . . . . . . . . . . . . . . . . 75

4.2 Drain current vs. reference electrode potential compared to ground for

different pH values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

LIST OF FIGURES xvii

4.3 Ag-AgCl reference electrode . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.4 Measured results for different HCl concentration of 0.5, 1, 1.5, 2 and

2.5mM from a voltage-mode readout circuit [30] . . . . . . . . . . . . . . 87

4.5 Current mode ISFET readout circuit which exhibits a linear relationship

between the output current and the concentration of analyte . . . . . . 88

4.6 Measured results for different HCL concentration of 0.5, 1, 1.5, 2 and

2.5mM from the current mode readout circuit in [31] when Vref = 0.44V 89

4.7 Measured results for different glutamate concentration of 0.5, 1, 1.5, 2

and 2.5mM from the current mode readout circuit in [31] when Vref = 0.26V 90

4.8 Measured results for different glutamate concentration of 0.5, 1, 1.5, 2

and 2.5mM from the current mode readout circuit in [31] when Vref = 0.21V 91

4.9 Diagram of coulometric titration . . . . . . . . . . . . . . . . . . . . . . 93

4.10 Diagram of iontophoresis . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

4.11 System used for iontophoresis experiment . . . . . . . . . . . . . . . . . 100

4.12 Measured result for three different injected amplitudes at 1µm distance

between the micropipette tip and the ISFET’s surface (insert is a ’Zoom

in’ of one period of the measured result) . . . . . . . . . . . . . . . . . . 101

4.13 Measured result for three different current pulse widths at a fixed injected

amplitude of 1uA and a 1µm distance between the micropipette tip and

the ISFET’s surface (insert is a ’Zoom in’ of one period of the measured

result) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.1 Postsynaptic current of (A) AMPA receptor, (B) NMDA receptor, (C)

GABAA receptor and (D) GABAB receptor [4] . . . . . . . . . . . . . . 111

LIST OF FIGURES xviii

5.2 A diagram based on Kaul’s experiment . . . . . . . . . . . . . . . . . . . 113

5.3 A circuit diagram for replacing a dysfunction central brain region with a

VLSI system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

5.4 Diagram of the trisynaptic circuit of the hippocampus . . . . . . . . . . 114

5.5 Conceptual representation of replacing the CA3 with a VLSI model . . . 115

5.6 Bernoulli cell circuit used for implementing variable rAMPA . . . . . . . 116

5.7 Bernoulli cell circuit used for implementing variable rNMDA . . . . . . . 118

5.8 Sigmoid circuit for B(V ) implementation . . . . . . . . . . . . . . . . . 119

5.9 Bernoulli cell circuit used for implementing variable rGABAA . . . . . . . 120

5.10 Bernoulli cell circuit used for implementing variables rGABAB and u . . . 122

5.11 Translinear current multiplication circuit . . . . . . . . . . . . . . . . . . 123

5.12 Circuit implementation of function u4

u4+Kd. . . . . . . . . . . . . . . . . 124

5.13 Circuit of the bionics postsynaptic chemical synapse . . . . . . . . . . . 126

5.14 Low transconductance gain OTA circuit . . . . . . . . . . . . . . . . . . 127

5.15 Measured vs. simulation results for the AMPA receptor . . . . . . . . . 129

5.16 Full schematic of a Bionics chemical synapse for the AMPA receptor . . 130

5.17 Measured vs. simulation results for the NMDA receptor . . . . . . . . . 132

5.18 Full schematic of a Bionics chemical synapse for the NMDA receptor . . 133

5.19 Measured vs. simulation results for the GABAA receptor . . . . . . . . 135

5.20 Full schematic of a Bionics chemical synapse for the GABAA receptor . 136

5.21 Measured vs. simulation results for the GABAB receptor . . . . . . . . 138

5.23 Microphotograph of the fabricated chemical synapse . . . . . . . . . . . 138

5.22 Full schematic of a Bionics chemical synapse for the GABAB receptor . 139

5.24 The photograph of bionics chemical synapse chip test and application

board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

5.25 Experimental setup for bionics chemical synapse chip . . . . . . . . . . . 140

5.26 Closed up picture of the glutamate ISFET and the tip of the micropipette141

B.1 PCB schematic for a bionics chemical synapse chip . . . . . . . . . . . . 156

B.2 PCB schematic for the OPAMP buffer and BNC, SMA ports . . . . . . 157

B.3 PCB schematic for the BNC, SMA ports I . . . . . . . . . . . . . . . . . 158

B.4 PCB schematic for the BNC, SMA ports II . . . . . . . . . . . . . . . . 159

xix

Chapter 1

Introduction

1.1 Motivation

In the past decade, digital electronics seems to have dominated in all aspects of the

electronics industry while analogue electronics appears to have faded away. However,

analogue electronics has flourished in the field of neuromorphic engineering, first pro-

posed by Mead in the late 1980s. Neuromorphic engineering has been applying analogue

electronics, which has the capability to process signals in real-time and at the same time

consume very little power, to emulate the models of neural systems.

The idea of a direct neural interface between a silicon chip and neural cells has been

progressively studied since the first neurochip was proposed by Maher et al. in 1998

[1]. Maher’s neurochip has the ability to both record and stimulate cultured neurons

with the same sensor. In 2005, DeMarse et al. presented a very interesting work where

cultured rat neural networks were trained to control a fighter aircraft, via an electrode

array, in a flight simulator [2]. These examples demonstrate the possibility of using

1

1.2. Research Objective 2

electronics circuit to interface with live neurons.

Spinal cord injury (SCI) refers to the damage of the spinal cord from a body wound

or shock, which causes loss of movements and sensations that may have resulted from

axon or synapse degeneration in the central nervous system (CNS). Research on the

medical treatment of SCI mainly focuses on the regeneration of neurons by applying

neuroregenerative substances to the damage area of the spinal cord [3, 4]. In the CNS,

glutamate is the vital neurotransmitter that has an important role in rapid synaptic

transmission. Implementation of an artificial glutamate receptor to detect extracellular

glutamate at the spinal cord could prove to be a useful alternative method for SCI

treatment.

The inspiration of this thesis is the possibility of using an artificial chemical synapse to

cure patients who suffer from spinal cord injury or paralysis by reconnecting the dam-

aged neural signal paths. The feasibility of this approach was demonstrated by Berger

et al., where a neuro-biomimetic silicon chip was used as a replacement neuron in the

hippocampus [5]. Berger’s chip was designed to match the behaviour of a CA3 neuron

in the hippocampal region. These in-vitro experiments of neural prostheses motivate

the author to use an artificial device, i.e. electronic circuits, to mimic the physiological

function of neurons and bypass the damaged neural path.

1.2 Research Objective

The objective of this research is to develop an artificial synapse that would not only

duplicate the function of chemical synapses but also has the capability to sense the actual

1.3. Overview 3

neurotransmitter concentration change. This synthetic synapse is aimed at patients

with spinal cord injury where it can be potentially used for the re-connection of the

damaged neural pathway. To achieve this, there are two essential topics that needs to

be investigated in this thesis:

1. The complexity of the chemical synapse model and the electronic circuit’s ability to

emulate it. The chemical synapse is modelled by a set of complex mathematical

functions [6] i.e. the first order differential equation, the sigmoid function and

the fourth power function. Therefore, suitable electronic circuits are required to

reproduce this behaviour while maintaining low power consumption for biomedical

application.

2. Sensing the chemical concentration in Molar unit vs. traditional ISFET readout

circuits. In a chemical synapse model, the neurotransmitter release is a brief

pulse of 1 mM in amplitude and 1ms in duration [6]. The traditional ISFET

readout circuit has a logarithmic relationship with concentration [7]. Furthermore,

a very fast chemical titration technique is required to generate the one millisecond

neurotransmitter test signal.

1.3 Overview

A chemical synapse in Fig.(1.1(a)) can be functionally transformed into an electronic

circuit called the Bionics Chemical Synapse, shown in Fig.(1.1(b)). In this work, the

bionics chemical synapse has been successfully implemented on an integrated circuit

with a separate or off-chip ISFET chemical sensor. This integrated circuit in CMOS

technology was designed according to Destexhe’s mathematical model of the chemical

synapse [6] and acts as the processing circuit, while the ISFET chemical sensor (ISFET)

1.3. Overview 4

operates as a neurotransmitter detector.

postsynapticpotential

postsynapticcell

actionpotential

presynapticcell

(a)

ChemicalSensor

SignalProcessor

Postsynapticoutput

(b)

Figure 1.1: Chemical synapse (a) and Electrical circuit implement (b)

A brief description of each chapter in this thesis is as follows:

1.3.1 Silicon Neuromorphic

The basic concept of neurons and the idea of bio-inspired neural systems are presented

in this chapter. Three important topics related to the physiology of the nervous sys-

tems - the neuron, the action potential and the synapse are described in detail. The

action potential models based on different mathematical functions are also examined,

especially for the Hodgkin and Huxley model [8] where its simulation results in MAT-

LAB are shown. Furthermore, the chemical synapse based on the Destexhe model [6]

is demonstrated with its simulation results. Finally, the silicon neuromorphic systems

based on the mathematical models of neurons and synapses are reviewed.

1.3. Overview 5

1.3.2 Low-gain OTA design

For the Destexhe’s chemical synapse model, a sub-nano Siemens transconductor is re-

quired where the synapse’s conductance gain is 0.1nS. The chapter begins with an

insightful analysis and explanation of an ordinary differential pair OTA. The macro

model analysis technique for MOSFET circuits is described for complex OTA circuits.

Both, the body input and drain current normalisation OTAs are analysed via this macro

model approach. In this chapter, a novel operational transconductance amplifier (OTA)

design, a combination of two transconductance amplifier topologies: the body input [9]

and the drain current normalisation [10], is presented. The fabricated OTA achieved

a 0.1nS transconductance gain, which is in agreement with the calculation and the

simulation result.

1.3.3 ISFET and Iontophoresis Technique

The principle of the ISFET is explained at the beginning of this chapter. The important

properties of the ISFET are described, including the ISFET’s operation, sensitivity,

and drift. Examples of the enzyme-immobilised ISFET are also given, with a detailed

immobilisation procedure for the glutamate ISFET outlined. The experimental result

on the non-linear characteristic of the traditional voltage-mode ISFET readout circuit

[7] to the ion concentration is shown. This non-linear relationship was overcome by using

a current-mode ISFET readout circuit [11]. Finally, the iontophoresis technique that

is capable of providing a fast ionic stimulus is introduced. This fast ionic perturbation

represents the change in neurotransmitter concentration in Destexhe’s chemical synapse

model [6].

1.3. Overview 6

1.3.4 Bio-inspired Chemical Synapse

In this chapter, the CMOS circuit implementation of the chemical synapse based on the

Destexhe’s model [6] is presented. Initially, the idea of a neural bridge to reconnect the

damaged neural pathway is introduced with two examples of neuron-electronic circuit

interface experiments. Each receptor i.e. AMPA, NMDA, GABAA and GABAB, was

formulated in the weakly inverted CMOS integrated circuit. The mathematical func-

tions of each receptor were realised with current-mode circuit techniques, for instance:

the Bernoulli cell for the first order differential equation, the OTA for the conductance

gain and the fourth power function by the translinear loop circuit. Finally, the glu-

tamate ISFET that functions as the neurotransmitter sensor, was connected with the

AMPA and NMDA synapse circuits to form the full chemical synapse circuit. How-

ever, due to the scarce availability of GABA oxidase to develop the GABA ISFET, the

GABAA and GABAB chemical synapse circuits were verified electronically.

References

[1] M. Maher, J. Wright, J. Pine, and Y.-C. Tai, “A microstructure for interfacing with

neurons: the neurochip,” in Engineering in Medicine and Biology Society, 1998.

Proceedings of the 20th Annual International Conference of the IEEE, vol. 4, 1998,

pp. 1698–1702 vol.4.

[2] T. B. DeMarse and K. P. Dockendorf, “Adaptive flight control with living neuronal

networks on microelectrode arrays,” in Neural Networks, 2005. IJCNN ’05. Pro-

ceedings. 2005 IEEE International Joint Conference on, vol. 3, 2005, pp. 1548–1551

vol. 3.

[3] A. R. Alexanian, M. G. Fehlings, Z. Zhang, and D. J. Maiman, “Transplanted

neurally modified bone marrowderived mesenchymal stem cells promote tissue pro-

tection and locomotor recovery in spinal cord injured rats,” Neurorehabilitation

and neural repair, vol. 25, no. 9, pp. 873–880, November/December 2011 Novem-

ber/December 2011.

[4] K. E. Thomas and L. D. F. Moon, “Will stem cell therapies be safe and effective for

treating spinal cord injuries?” British medical bulletin, vol. 98, no. 1, pp. 127–142,

June 01 2011.

7

REFERENCES 8

[5] T. W. Berger, A. Ahuja, S. H. Courellis, S. A. Deadwyler, G. Erinjippurath, G. A.

Gerhardt, G. Gholmieh, J. J. Granacki, R. Hampson, M. C. Hsaio, J. Lacoss, V. Z.

Marmarelis, P. Nasiatka, V. Srinivasan, D. Song, A. R. Tanguay, and J. Wills,

“Restoring lost cognitive function,” Engineering in Medicine and Biology Magazine,

IEEE, vol. 24, no. 5, pp. 30–44, 2005.

[6] Z. F. M. Alain Destexhe and T. J. Sejnowski, Kinetic models of synaptic transmis-

sion., 2nd ed., ser. Methods in Neuronal Modeling (2nd ed.). Cambridge, MA:

MIT Press, 1998, pp. 1–26.

[7] H. Nakajima, M. Esashi, and T. Matsuo, “The pH response of organic gate ISFETs

and the influence of macro-molecule adsorption,” Nippon Kagaku Kaishi, vol. 10,

pp. 1499–1508, 1980.

[8] A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current

and its application to conduction and excitation in nerve,” J Physiol, vol. 117,

no. 4, pp. 500–544, August 28 1952.

[9] R. Sarpeshkar, R. F. Lyon, and C. Mead, A low-power wide-linear-range transcon-

ductance amplifier, ser. Neuromorphic Systems Engineering: Neural Networks in

Silicon. Norwell, MA, USA: Kluwer Academic Publishers, 1998, pp. 267–313.

[10] S. P. DeWeerth, G. N. Patel, and M. F. Simoni, “Variable linear-range subthreshold

OTA,” Electronics Letters, vol. 33, no. 15, pp. 1309–1311, 1997.

[11] L. M. Shepherd and C. Toumazou, “A biochemical translinear principle with weak

inversion ISFETs,” Circuits and Systems I: Regular Papers, IEEE Transactions

on, vol. 52, no. 12, pp. 2614–2619, 2005.

Chapter 2

Silicon Neuromorphic

2.1 Introduction

Since the late 1980s when Carver Mead published his work on an analogue electronic

cochlear [1], many researchers have increasingly turned their attention to the field of

bio-inspired electronic circuits. The term neuromorphic, introduced by Mead, refers to

neural systems that have been created using electronic circuits. These circuits can be

either on an analogue or digital platform. In the field of neuromorphic VLSI, there are

many recent studies that have designed integrated circuits to assist in hearing [2, 3],

visual perception [4, 5, 6] and the sense of smell [7]. It can be said that neuromorphic

engineering is one of the prominent applications in VLSI designs.

In this chapter, the definition and the structure of the neuron are discussed. It is im-

portant to study the biochemical properties of a neuron, such as the ion channels and

the Nernst equation, to understand the behaviour of neurons. The trigger signal in

the neuronal system, called the action potential, will also be described in detail and its

9

2.2. Neuron 10

mathematical model - the Hodgkin-Huxley, the integrated-fire and the Morris-Lecar,

examined. The Hodgkin and Huxley model, in particular, will be expanded to show the

individual chemical current channels and their behaviour in simulation.

Another important part of this chapter is the synapse and its mathematical model. Bio-

logical details of the chemical and electrical synapses will be described and, in particular,

the Destexhe’s chemical synapse model which was used as the basis for electronic circuits

implementation in this thesis. Furthermore, its simulation results from the mathemat-

ical simulator (MATLAB) will be presented. Finally, examples of the mathematical

model-based neuron implementation using analogue electronics will be given.

2.2 Neuron

The brain, vertebrate spinal cords and peripheral nerves are constructed from the same

vital parts called neurons. The function of a neuron is to couple neural signals from

the brain to the targeted organ. Neural signals received at the dendrites of the neuron

are re-transmitted along the axon via an electrochemical mechanism [8]. The main

components of a typical neuron consist of the dendrites, a soma, a nucleus and the

axons as shown in Fig.(2.1).

Another unique property of neurons is the ability to transmit electrical signals over long

distances [9]. These signals travel through the cell membrane, which contains several

types of ion channels that interact with the changes in the transmembrane potential. A

transient pulse of charges across this transmembrane is called an action potential [10].

2.2. Neuron 11

Dendrite

Nucleus

Soma

Axon

Myelin Sheath Schwann cell

Node of Ranvier

Axon terminal

Figure 2.1: The structure of a neuron

2.2.1 Ion channels and electrical properties of membranes

Ion channels are membrane proteins or an assembly of several proteins, which are di-

rectly responsible for the transport of inorganic ions. The most distinct characteristic

of these channels is that over a million ions can cross a single ion channel per sec-

ond. The function of an ion channel is to allow a particular inorganic ion, i.e. Na+,

K+, Ca2+ or Cl– to diffuse down their electrochemical gradients across the lipid bilayers.

The operation of the ion channels are controlled by the process of ion selectivity and

the fluctuation between their open and closed states. The first property signifies that

the ion channels will only permit certain inorganic ions to pass but not others. The

second significant property indicates a gate mechanism of the ion channels, which opens

briefly and then closes. There are many specific stimuli that actuate the ion channel’s

gate. Some of the more well-known stimuli are:

� Voltage-gated channels - ions channels that open owning to changes in the mem-

brane potential.

2.2. Neuron 12

� Mechanical gated channels - ion channels that open under a mechanical stress.

� Ligand-gated channels - ion channels that are stimulated by the binding of a

ligand. This ligand can be either an extracellular mediator (a neurotransmitter

or transmitted-gated channel), an intracellular mediator (ion-gated channel) or a

nucleotide (nucleotide-gated channels).

Most ion channels are sensitive to K+ ions. When these ion channels operate, their

common function is to make the plasma membrane more permeable to K+ ions. This

behaviour plays a vital role in the regulation of the membrane potential.

The potential difference between the inside and the outside of the membrane, termed

the membrane potential, arises from the difference in electrical charges between the two

sides of the membrane. In humans, the Na+ - K+ pump assists in the maintenance of

the osmotic balance across the cell membrane by keeping a lower concentration of Na+

ions on the inside compared to the outside the cell.

2.2.2 Nernst Equation

The flow of any ions through a membrane channel is driven by the electrochemical gra-

dient. This gradient is influenced by both the voltage and the concentration gradient

of ions across the cell membrane.

When the influence of these two factors are balanced, the electrochemical gradient for

the ion is zero. The net flow of the ion channel is also zero. The membrane potential

(voltage gradient) at this equilibrium is given by the Nernst equation in eq.(2.1).

2.3. Action potential and its model 13

Vmem =RT

zFlnCoCi

(2.1)

where Vmem is the equilibrium membrane potential, CoCi

is the ratio of the outside to

the inside ion concentration, R is the gas constant (8314.4mJ/K·mol), T is the absolute

temperature in Kelvin, F is the Faraday’s constant 96, 485C/mol and z is the charge of

the ion.

For an animal cell, the potential difference across the plasma membrane at equilibrium

varies from -20mV to -200mV depending on the organism and the cell type. In hu-

man beings, this potential at equilibrium, termed the resting potential is given by the

Goldman equation shown in eq.(2.2)

Vmem = 58mV lnPK [K+]out + PNa[Na

+]out + PCl[Cl−]out

PK [K+]in + PNa[Na+]in + PCl[Cl−]in(2.2)

where PK , PNa and PCl are the relative membrane permeability for K+, Na+ and

Cl− ions. [K+], [Na+] and [Cl−] are the concentration of the potassium, sodium and

chloride ion. The subscriptions out and in refer to the outside and the inside of the

membrane, respectively.

2.3 Action potential and its model

2.3.1 Generation of Action Potential

An action potential is triggered when the plasma membrane potential rises above its

resting value. This event is termed as depolarisation. Depolarisation is the consequence


of a neurotransmitter-triggered response by the cell body. Owing to this depolarisation,

the voltage-gated channel for the sodium ions opens and allows Na+ ions to move inside

the cell. The amount of this migration is in accordance with its electrochemical gradient.

This open state of the Na+ channel remains until the membrane potential rises to

+50mV from the -70mV resting potential.

At a membrane potential of +50mV, a new equilibrium state is reached. However, the

duration of this peak is short owing to an automatic inactivation of the Na+ channels.

This mechanism forces the sodium channels to shut rapidly even when the membrane

is depolarised. With the sodium channels closed, the activation of the K+ channels

begins to bring the membrane potential back to the resting level (-70mV). The opening

of the K+ channels causes the K+ ions to dominate over the Na+ ions, which drives

the membrane potential back towards the K+ ion equilibrium point. Fig.(2.2) shows a

typical profile of an action potential as described above.

2.3.2 Membrane potential model

The membrane potential has been modelled mathematically in various forms. The very

first model, the integrate and fire model, was published in 1907 by Lapicque [11]. No

correlation between the membrane potential and the biophysical details was given in

this model. The first qualitative membrane potential with correlation to the biophys-

ical details was constructed from the experiment on a squid giant axon by Hodgkin

and Huxley in 1952 [12]. The Hodgkin and Huxley model is based on three main ionic

currents - sodium, potassium and leakage. More details on the Hodgkin and Huxley

model will be described in a later section.


Synaptic vesicle

Voltage-gated

Ca++ channels

Neurotransmitter

receptors

Postsynaptic

neuron

Presynaptic

neuron

Axon

terminal

Synaptic

cleft

Dendrite

spine

Vpre

Vpost

Threshold of

excitation

Na+ channels

open, Na+

begins to enter

cell

K+ channels

open, K+

begins to leave

cell

Na+ channels

become

refractory, no

more Na+

enters cell

K+ continues to

leave cell and

causes membrane

potential to return

to the resting potential

K+ channels close,

Na+ channels rest

Extra K+ outside

Diffuses away

-70

+40

1

2

3

4

5

6

Me

mb

ran

e p

ote

nti

al

(mV

)

time

Figure 2.2: Typical Nerve Action Potential

The mechanism of the potassium and the sodium channels in the Hodgkin and Huxley

model are described by non-linear, time-dependent functions. Thus, the computational

algorithm or the electronic circuit implementation of the Hodgkin and Huxley model will

be complex. More recently, there has been several attempts to re-model the membrane

potential with a less complex mathematical function while still exhibiting the neuron

behaviour in the Hodgkin and Huxley model. In this section, three other membrane po-

tential models will be described: Integrate-and-Fire, FitzHughNagumo and MorrisLecar

model.


� Integrate-and-Fire model: the simplest and the first model, proposed in 1907 by

Lapicque [11]. This model is based on the current and voltage of a capacitor,

given by:

I(t) = CmdV

dt(2.3)

where I(t) is the applied current, Cm is the membrane capacitance and V is the

membrane potential. When the current is applied, the membrane potential rises

until the threshold voltage (Vth) is reached. After reaching Vth, the membrane

potential will reset itself to the resting potential. From the hardware implemen-

tation aspect, the integrate and fire model is the most compact among the neuron

models.

� FitzHugh-Nagumo model: the model was published in 1961 by FitzHugh [13] and

later realised using electrical circuits by Nagumo et al. [14].

dV

dt= V − V 3

3−W + I

dW

dt= 0.08(V + 0.7− 0.8W )

(2.4)

where W is the recovery variable and I is the stimulus current.

The FitzHugh-Nagumo model can be classified as a reduced version of the Hodgkin

and Huxley model because the three current compartments (Na+, K+ and leakage)

have been reduced into a single variable equation.

� Morris-Lecar model: another simplified model of Hodgkin and Huxley. There are

three current channels in this model: Ca2+, K+ and leakage. The equations for


this model are [15]:

CmdV

dt= −gCaMss(V )(V − ECa)− gKW (V − EK)− gL(V − EL) + I

dW

dt=Wss(V )−W

τW (V )

Mss =1 + tanh[V−V1V2

]

2

Wss =1 + tanh[V−V3V4

]

2

τW (V ) = τ0sech(V − V3

2V4)

(2.5)

where W is the recovery parameter. gCa and gK are the conductance of calcium

and potassium channels, respectively. ECa and EK are the equilibrium potential

for calcium and potassium. Mss and Wss are the open state probability.

The Morris-Lecar model preserves the chemical channels as in the Hodgkin and

Huxley model. More importantly, the open state equation for each channel is less

complex and is more straightforward to implement than the Hodgkin and Huxley

model.

2.3.3 Hodgkin and Huxley model

The first qualitative mathematical model of an action potential was published by Alan

L. Hodgkin and Andrew Huxley in 1952 [12, 16, 17, 18, 19]. From the voltage clamp

experiment along the axon of a giant squid, Hodgkin and Huxley observed that the

electrical current across the cell membrane depended on two factors:

1. The resistance of the cell membrane, and


2. The capacitance of the cell membrane

gNa+ gK+ gLeak

ENa+ EK+ ELeak

INa+ IK+ ILeak

VmCm

Extracellular

Intracellular

Figure 2.3: Equivalent electrical circuit for the Hodgkin-Huxley model

Fig.(2.3) illustrates the components of the Hodgkin-Huxley model. The capacitance

Cm is the portrayal of a lipid bi-layer. The non-linear electrical conductances (gk+ and

gNa+) control the voltage-gated ion channels. The leakage channel is represented by the

linear conductance (gLeak). The equilibrium potential of each ion (ENa, EK and EL)

represents their respective electrochemical gradient. The total current of the membrane

consists of the capacitive current and the resistive current.

Current component of Hodgkin and Huxley model

With the voltage-dependent property of a capacitor, the capacitance current (Icap), the

membrane capacitance (Cm) and membrane potential (vm) can be derived as:

Icap = Cmdvmdt

(2.6)


The resistive current is the voltage-dependent current (both membrane and equilibrium

potential). The equilibrium potential of individual channels can be calculated from

the Nernst equation (eq.(2.1)). From the circuit point of view, the ionic current in

the membrane is directly proportional to the difference between the membrane and

equilibrium potential, as shown in eq.(2.7).

Iion = gion(vm − Eion) (2.7)

The total membrane current (Im) for the model proposed by Hodgkin and Huxley can

be given as:

Im = Icap + Iion (2.8)

Im = Cmdvmdt

+ gion(vm − Eion) (2.9)

Im = Cmdvmdt

+

INa︷︸︸︷gNa(t)(vm − ENa) +

IK︷︸︸︷gK(t)(vm − EK) +

IL︷︸︸︷gL(vm − EL) (2.10)

where gNa(t), gK(t) and gL are the conductance of the sodium, potassium and leakage

channel, respectively. ENa, EK and EL are the equilibrium potential of the sodium,

potassium and leakage channel.

The experiment of Hodgkin and Huxley also concluded that gNa(t) and gK(t) are non-

linear conductances whilst gL is linear. The time-dependence of the potassium and

sodium channels was modelled by introducing a new variable that refers to the proba-


bility of the ionic gating process. This will be shown in the next section.

Note that the lowercase, vm, is the difference in the membrane potential, Vm(t), and its

resting value, Vm(rest). Thus, the definition of vm is:

vm(t) = Vm(t)− Vm(rest) (2.11)

From eq.(2.11), vm mathematically differs from Vm(t) by a constant. This means that

the time-derivation of vm is equal to the corresponding derivatives of Vm(t).

Mathematical model for the potassium channel

The potassium conductance gK(t, vm) is the fixed maximum conductance (when all

channels are open), gK , multiplied by n4: the fraction of the open channels (0 < n < 1).

Thus,

gK(t, vm) = gKn4(t, vm) (2.12)

The variable n can be derived from the first order kinetics:

dn(t, vm)

dt= αn(vm)(1− n)− βn(vm)n (2.13)

From curve fitting, the rate constants αn(vm) and βn(vm) are:

αn =0.01(10 + vm)

exp(10+vm10 )− 1

(2.14)


and

βn = 0.125 exp(vm80

) (2.15)

where vm is in mV and α, β are in (milli-second)−1. The potassium channel current is

given by:

IK = gKn4(vm − EK) (2.16)

Mathematical model for the sodium channel

The ionic current for the sodium channel has a similar model to the potassium channel,

except that there are two control probability variables: m (activation) and h (inactiva-

tion), where:

gNa(t, vm) = gNam3(t, vm)h(t, vm) (2.17)

Both parameters follow the first-order differential equation similar to the variable n in

the potassium channel as:

dm(t, vm)

dt= αm(vm)(1−m)− βm(vm)m (2.18)

and

dh(t, vm)

dt= αh(vm)(1− h)− βh(vm)h (2.19)


The rate constants - αm, βm, αh and βh - were chosen from the curve fitting as:

αm =0.1(25 + vm)

exp(25+vm10 )− 1

(2.20)

βm = 4 exp(vm18

) (2.21)

and

αh = 0.07 exp(vm20

) (2.22)

βh =1

exp(30+vm10 ) + 1

(2.23)

where vm is in mV and α, β are in (milli-second)−1. The sodium channel current is

given by:

INa = gNam3h(vm − ENa) (2.24)

Mathematical model for the leakage channel

As stated earlier, the conductance of the leakage channel is considered as a constant.

Thus, the leakage channel current is given by:

IL = gL(vm − EL) (2.25)


The value of the variables mentioned in the sodium, potassium and leakage channel is

shown in Table (2.1).

Table 2.1: Hodgkin and Huxley nerve axon model parametersConstant Name Units Values

Cm Membrane capacitance µF/cm2 1 to 2.8

ENa Sodium equilibrium potential mV Vm(rest) + 115

EK Potassium equilibrium potential mV Vm(rest)− 12

EL Leakage equilibrium potential mV Vm(rest)− 10.613

gNa Sodium maximum conductance mS/cm2 120

gK Potassium maximum conductance mS/cm2 36

gL Leakage maximum conductance mS/cm2 0.3

2.3.4 Single Compartment

In Table(2.1), some units of the Hodgkin and Huxley model parameter are per unit area.

Therefore, to synchronise the Hodgkin and Huxley model with the chemical synapse, a

single neuron model, those units has to be transformed for a single compartment neuron.

Firstly, the exact area of a single neuron needs to be calculated. A single compartment of

neurons is 10µm in diameter, 10µm in length (i.e. area of single neuron is π×10−6cm2)

[20]. The transformed parameters from Table(2.1) are shown in Table(2.2).

Table 2.2: Transformed Hodgkin and Huxley axon model parametersConstant Name Units Values

Cm Membrane capacitance pF 3.14159 to 8.79645

gNa Sodium maximum conductance nS 376.9911184

gK Potassium maximum conductance nS 113.0973355

gL Leakage maximum conductance nS 0.9424777961

Furthermore, the unit of vm in eq.(2.14), eq.(2.15), eq.(2.20), eq.(2.21), eq.(2.22) and

eq.(2.23) is mV. To standardise this unit, these equations need to be transformed into

Volts. The transformations are shown in eq.(2.26) to eq.(2.31).


αn =104(0.01 + vm)

exp(0.01+vm0.01 )− 1

(2.26)

βn = 125 exp(vm

0.08) (2.27)

The graph plots in MATLAB of eq.(2.26) and (2.27) are shown in Fig.(2.4).

(a) (b)

Figure 2.4: Rate constants (a) αn and (b) βn

The activation of the open state for the potassium channel (n) is a function of αn and

βn i.e. the first order differential equation as shown in eq.(2.13). The plot of the n

variable is shown in Fig.(2.5).

αm =105(0.025 + vm)

exp(0.025+vm0.01 )− 1

(2.28)

βm = 4×103 exp(vm

0.018) (2.29)


Figure 2.5: Activation of potassium channel (n)


(a) (b)

Figure 2.6: Rate constants (a) αm and (b) βm

The activation variable of the sodium channel (m) is a function of αm and βm i.e. the

first order differential equation as shown in eq.(2.18). The plot of the variable m is

shown in Fig.(2.7).


Figure 2.7: Activation of the sodium channel (m)

αh = 70 exp(vm

0.02) (2.30)

βh =103

exp(0.03+vm0.01 ) + 1

(2.31)


(a) (b)

Figure 2.8: Rate constant (a) αh and (b) βh


The inactivation variable for the sodium channel (h) is a function of αh and βh i.e.

the first order differential equation as shown in eq.(2.19). The plot of the variable h is

shown in Fig.(2.9).

Figure 2.9: Inactivation of the sodium channel (h)

The action potential according to eq.(2.10) was also plotted. Its result is illustrated in

Fig.(2.10).

(a) (b)

Figure 2.10: (a) The action potential (vm) observed when applied with (b) the totalmembrane current (Im)

2.4. The Synapse 28

2.4 The Synapse

Communication between neurons is achieved via the transmission of action potentials.

This transmission is facilitated by synapses which acts as the medium. Synapses have

a bulb-like structure and their function is to interconnect neurons with other targeted

neurons. The synapse is the crucial part of a the neural communication system because

it allows a neuron to instantly relay signals to one or more other neurons [21]. Synapses

can be categorised into two types: electrical and chemical.

2.4.1 Electrical Synapse

For the electrical synapse shown in Fig.(2.11), the depolarisation of the presynaptic

neuron is directly coupled to the postsynaptic neuron without any delay. The pre- and

postsynaptic membrane of the electrical synapse are separated by a small gap junction

(3.5nm). The transmission of action potentials for this instance is simply a directly

connected ionic current.

Owing to the ionic current movement at the gap junction, the direction of the trans-

mission at the electrical synapses can be bidirectional. Other remarkable properties of

electrical synapses are their speed and reliability. The delay due to this type of synaptic

transmission is very small and can be negligible.

2.4.2 Chemical Synapse

In contrast with electrical synapses where the pre- and postsynaptic neurons are ad-

hered to each other, the pre- and postsynaptic membrane of chemical synapses shown

in Fig.(2.12) have a larger separation (20-40 nm), called a synaptic cleft. As a result,

2.4. The Synapse 29

Gap junction

Figure 2.11: Electrical synapse

chemical synapses rely on the release of neurotransmitters from the presynaptic neuron.

The neurotransmitters are stored in the synaptic vesicles at the presynaptic terminal.

Once these neurotransmitters are emitted into the synaptic cleft, they will bind to a

specific receptor at the postsynaptic neuron.

The detail of the chemical synaptic events from the pre- to the postsynaptic cell is

summarised as [21]:

1. In the bouton of the postsynaptic neuron, the neurotransmitters are filled within

the vesicles. Most of these vesicles are incapacitated. When the presynaptic action

potential reaches the terminal arborisation of a bouton, the depolarisation induces

the voltage-gated calcium channel proteins to open and accept Ca2+ ions, which

causes the concentration of Ca2+ to increase from 100 nM to 100 µM.

2.4. The Synapse 30

Synaptic vesicle

Voltage-gatedCa++ channels

Neurotransmitterreceptors

Presynapticneuron

Postsynapticneuron

Axonterminal

Synapticcleft

Dendritespine

Figure 2.12: Chemical synapse

2. An increase of intracellular [Ca2+] causes the vesicles to deliquesce and release

their neurotransmitters into the synaptic cleft. This process is called exocytosis.

3. The released neurotransmitter molecules bind to the receptors on the postsynaptic

cell membrane. This binding process leads to the opening and closing of ion

channels. The resulting ionic flux causes the membrane conductance and the

membrane potential of the postsynaptic cell to fluctuate.

Table (2.3), below, summarises the contrasting properties of electrical and chemical

synapses.

2.4.3 Biological model for the chemical synapse

Model of neurotransmitter release

The relationship between the presynaptic action potential and the release of the neu-

rotransmitter has been described in a mathematical model [22], which was simplified

2.4. The Synapse 31

Table 2.3: Summary Properties of SynapsesProperty Electrical Synapse Chemical Synapse

Distance between pre- 3.5 nm 16-20 nmand postsynaptic cell membranes

Cytoplasmic continuity between Yes Nopre- and postsynaptic cells

Ultrastructural components Gap-junction channels Presynaptic vesicles

Agent of transmission Ion current Chemical transmitter

Synaptic delay Negligible 0.3-5 ms, depending

Direction of transmission Generally bidirectional Generally unidirectional

from the calcium-induced release model [23]. Eq.(2.32) shows the relationship between

the neurotransmitter concentration [T ] and the presynaptic voltage Vpre as:

[T ](Vpre) =Tmax

1 + exp [−(Vpre − Vp)/Kp](2.32)

where Tmax is the maximal concentration of the neurotransmitter in the synaptic cleft,

Kp is the steepness and Vp is the half-activated function.

Kinetic model of the synapse

The relationship between the postsynaptic response and the neurotransmitter concen-

tration was proposed by Destexhe et al. [20]. This response can be described in the

first order kinetic regime as:

R+ Tα

GGGGGBFGGGGG

βTR∗ (2.33)

where R and TR∗ are the unbound and bound state of the postsynaptic receptors, re-

spectively. α and β are the forward and backward rate constant for the neurotransmitter

binding. The fraction of bound receptor for this model is expressed using the law of

2.4. The Synapse 32

mass action [24], stated as:

dr

dt= α[T ](1− r)− βr (2.34)

where [T ] is the concentration of the neurotransmitter and r is defined as the fraction of

the receptors in the open state. This neurotransmitter concentration is simplified and

modelled as a pulse with a 1 ms duration and a 1 mM amplitude.

Model for the postsynaptic transmission

The mathematical model of the postsynaptic transmission has been simplified from the

Markov model of the postsynaptic current [25]. The postsynaptic membrane voltage

(Vpost) consists of the voltage-gated ion channels current (Iion) and the synaptic current

(Isyn), as shown in eq.(2.35) and eq.(2.36).

CmdVpostdt

= −(Iion + Isyn) (2.35)

Isyn = gsyn(t)(Vpost − Esyn) (2.36)

where Cm is the membrane capacitance, gsyn(t) is the time-dependent synaptic conduc-

tance and Esyn is the reversal potential of the channel.

There are four types of receptors that have been modelled [25]: AMPA, NMDA,

GABAA and GABAB. AMPA and NMDA are classified as the EPSP (excitatory

postsynaptic potential) whilst GABAA and GABAB are considered as the IPSP (in-

2.4. The Synapse 33

hibitory postsynaptic potential). The postsynaptic current for each receptor is described

as:

AMPA receptor:

drAMPA

dt= αAMPA [T ](1− rAMPA)− βAMPArAMPA (2.37)

IAMPA = gAMPA

rAMPA(V − EAMPA) (2.38)

where gAMPA

is the maximal conductance (approximately 0.35− 1.0 nS), rAMPA is the

fraction of the receptor in the open state, V is the postsynaptic potential and EAMPA

is the reversal potential (= 0mV). Obtaining the best fit from the kinetic scheme,

αAMPA = 1.1 × 106 and βAMPA = 190. The plot of the variable rAMPA is shown in

Fig.(2.13).

Figure 2.13: MATLAB simulation of rAMPA

2.4. The Synapse 34

NMDA receptor:

drNMDA

dt= αNMDA [T ](1− rNMDA)− βNMDArNMDA (2.39)

B(V ) =1

1 + exp (−0.062V )[Mg2+]o3.57

(2.40)

INMDA = gNMDA

B(V )rNMDA(V − ENMDA) (2.41)

where gNMDA

is the maximal conductance (approximately 0.01 − 0.6 nS), B(V ) is the

magnesium block, [Mg2+]o is the external magnesium concentration (1 to 2 mM in

physiological conditions), rNMDA is the fraction of the receptors in the open state, V

is the postsynaptic potential and ENMDA is the reversal potential (= 0mV). Obtaining

the best fit from the kinetic scheme, αNMDA = 7.2× 104 and βNMDA = 6.6. The plot of

the variable rNMDA is shown in Fig.(2.14).

Figure 2.14: MATLAB simulation of rNMDA

2.4. The Synapse 35

GABAA receptor:

drGABAAdt

= αGABAA [T ](1− rGABAA )− βGABAA rGABAA (2.42)

IGABAA = gGABAA

rGABAA (V − EGABAA) (2.43)

where gGABAA

is the maximal conductance (approximately 0.25-1.2 nS), rGABAA is the

fraction of the receptors in the open state, V is the postsynaptic potential and EGABAA

is the reversal potential (= -70 mV). Obtaining the best fit from the kinetic scheme,

αGABAA = 5.3 × 105 and βGABAA=180. The plot of the variable rGABAA is shown in

Fig.(2.15).

Figure 2.15: MATLAB simulation of rGABAA

GABAB receptor:

drGABABdt

= K1 [T ](1− rGABAB )−K2rGABAB (2.44)

2.4. The Synapse 36

du

dt= K3rGABAB −K4u (2.45)

IGABAB = gGABAB

u4

u4 +Kd(V − EGABAB

) (2.46)

where gGABAB

is the maximal conductance (approximately 1 nS), rGABAB is the fraction

of the activated receptors, V is the postsynaptic potential, u is the concentration of

activated G-protein, and EGABABis the reversal potential (= -95 mV). From curve

fitting, the following values were obtained: Kd = 100µM4, K1 = 9×104 M−1s−1,

K2 = 1.2 s−1, K3 = 180 s−1, K4 = 34 s−1 and n = 4 binding site. The plot of the

variable rGABAA is shown in Fig.(2.16).

Figure 2.16: MATLAB simulation of rGABAB

2.4.4 Postsynaptic simulation

The postsynaptic potential of the AMPA, NMDA, GABAA and GABAB receptors

are simulated according to eq.(2.35). The terms Iion and Isyn in this equation refer to

the Hodgkin and Huxley ionic current and the synaptic receptor current, respectively.

The resting potential in this case is assumed to be 100mV. The Hodgkin and Huxley

2.4. The Synapse 37

parameters for this resting potential are:

αn =104(0.11 + Vm)

exp(0.11+Vm0.01 )− 1

βn = 125 exp(0.1 + Vm

0.08)

dn

dt= αn(1− n)− βnn

αm =105(0.125 + Vm)

exp(0.125+Vm0.01 )− 1

βm = 4×103 exp(0.1 + Vm

0.018)

dm

dt= αm(1−m)− βmm

αh = 70 exp(0.1 + Vm

0.02) βh =

103

exp(0.13+Vm0.01 ) + 1

dh

dt= αh(1− h)− βhh

The potassium, sodium and leakage currents for a 100mV resting potential are:

IK = gKn4(Vm − 0.112) INa = gNam

3h(Vm + 0.015) IL = gL(Vm − 0.089387)

The simulation of the postsynaptic transmission for the AMPA, NMDA, GABAA and

GABAB receptors are shown below.

AMPA postsynaptic simulation

The postsynaptic simulation of the AMPA receptor was based on:

CmdVmdt

= −INa − IK − IL − IAMPA

IAMPA = gAMPArAMPA(Vm − EAMPA)(2.47)

where the reversal potential for AMPA (EAMPA) with a 100mV resting potential is

170mV and the maximal conductance for AMPA (gAMPA) is 0.1nS. The MATLAB

2.4. The Synapse 38

simulation of the AMPA receptor is shown in Fig.(2.17) and (2.18).

(a) (b)

Figure 2.17: (a) Single spike of AMPA neurotransmitter and (b) its postsynaptic re-sponse

(a) (b)

Figure 2.18: (a) Four spikes of AMPA neurotransmitter and (b) its postsynaptic re-sponse

NMDA postsynaptic simulation

The postsynaptic simulation of the NMDA receptor was based on:

CmdVmdt

= −INa − IK − IL − INMDA

INMDA = gNMDAB(Vm)rNMDA(Vm − ENMDA)

B(Vm) =1

1 + exp (−0.062Vm)[Mg2+]o3.57

(2.48)

2.4. The Synapse 39

where the reversal potential for NMDA (ENMDA) with a 100mV resting potential is

170mV and the maximal conductance for NMDA (gNMDA) is 0.1nS. The MATLAB

simulation of the NMDA receptor is shown in Fig.(2.19) and (2.20).

(a) (b)

Figure 2.19: (a) Single spike of NMDA neurotransmitter and (b) its postsynapticresponse

(a) (b)

Figure 2.20: (a) Four spikes of NMDA neurotransmitter and (b) its postsynaptic re-sponse

GABAA postsynaptic simulation

The postsynaptic simulation for the GABAA receptor was based on:

2.4. The Synapse 40

CmdVmdt

= −INa − IK − IL − IGABAA

IGABAA = gGABAArGABAA(Vm − EGABAA)(2.49)

where the reversal potential for GABAA (EGABAA) with a 100mV resting potential is

90mV and the maximal conductance for GABAA (gGABAA) is 0.1nS. The MATLAB

simulation of the GABAA receptor is shown in Fig.(2.21) and (2.22).

(a) (b)

Figure 2.21: (a) A single spike of GABAA neurotransmitter and (b) its postsynapticresponse

(a) (b)

Figure 2.22: (a) Four spikes of GABAA neurotransmitter and (b) its postsynapticresponse

2.4. The Synapse 41

GABAB postsynaptic simulation

The postsynaptic simulation for the GABAB receptor was based on:

CmdVmdt

= −INa − IK − IL − IGABAA

IGABAB = gGABAB

u4

u4 +Kd(V − EGABAB

)

drGABABdt

= K1 [T ](1− rGABAB )−K2rGABABdu

dt= K3rGABAB −K4u

(2.50)

where the reversal potential for GABAB (EGABAB ) with a 100mV resting potential is

75mV and the maximal conductance for GABAB (gGABAA) is 0.1nS. The MATLAB

simulation of the GABAB receptor is shown in Fig.(2.23).

(a) (b)

Figure 2.23: (a) Ten spikes of GABAB receptor and (b) its postsynaptic response

The mathematical model of the chemical synapse receptors, addressed in this section

(i.e. AMPA in eq.(2.47), NMDA in eq.(2.48), GABAA in eq.(2.49) and GABAB in

eq.(2.50), will be implemented using analogue current-mode CMOS circuits operated in

weak inversion region. These electronics circuit implementations will be described in

Chapter 5.

2.5. Silicon neuromorphic circuits 42

2.5 Silicon neuromorphic circuits

Since the idea of implementing neuromorphic systems on silicon was initiated by Mead

and his collaborators in the late 1980s and the early 1990s [1, 26], bio-inspired systems

on the CMOS platform has captured many researchers’ imagination.

In this section, reviews of the silicon neurons (mostly based on the Hodgkin-Huxley

model) and the synapse models will be presented.

2.5.1 Silicon Neurons

The Hodgkin and Huxley model is a conductance-based neuron model. This model was

firstly implemented on silicon by Mahowald et al. in 1991 [27]. Mahowald’s silicon neu-

ron consists of a CMOS operational transconductance amplifier (OTA), a differential

pair and a current mirror operated in the weak inversion region. These circuit compo-

nents were able to duplicate the non-linear, time-dependent functions in the Hodgkin

and Huxley model.

According to the realisation of a current-mode integrator which performed mathemat-

ically the Bernoulli’s equation, Drakakis et al. [28] and Toumazou et al. [29] demon-

strated that this Bernoulli cell can duplicate the activation variable n (in eq.(2.13)) of

the potassium ion channel in the Hodgkin and Huxley model, as shown in Fig.(2.24)

Later in 2007, Lazaridis et al. [30] produced an implementation of the rate constant

αn, in eq.(2.14), in a subthreshold CMOS circuit. This implementation consisted of a

CMOS operational transconductance amplifier, the E-cell and a translinear loop circuit.


Na+

chan

nel

g L

INa IL

EL

Cmem

nI

nn II

Iout

I0

I0IK(Iout1)

K+ channel

Figure 2.24: Hodgkin and Huxley implementation on CMOS of Toumazou et al. [29]

Lazaridis ’s work illustrated the ability to fully implement the Hodgkin and Huxley neu-

ron model on the CMOS platform.

Owing to the complexity of the mathematics required to calculate the variables in the

Hodgkin and Huxley model that is reflected by the circuit realisation, Farquhar et al.

proposed a contrasting idea to implement the Hodgkin and Huxley model with less

components than previous designs [31]. In Farquhar’s work, the similarity between

the non-linear characteristics of the MOSFET current and those of the variables in the

Hodgkin and Huxley model were compared. As a result, Farquhar succeeded in creating

a CMOS version of the Hodgkin and Huxley neuron with just six MOSFETs and three

capacitors.


2.5.2 Silicon Synapses

Ludovic et al. [32] created a Bi-CMOS circuit to duplicate the fraction of the receptors

in the open state (r), in eq.(2.34). This was achieved by transforming eq.(2.34) into an

exponential decay function, where a resistive-capacitive circuit was employed together

with a bipolar junction transistor (BJT) to formulate this function. This work can be

considered as the first CMOS synapse based on the model of Destexhe [22].

In 2006, Lazaridis et al. applied the Bernoulli cell [33] to duplicate r with a weakly

inverted CMOS circuit. The Bernoulli cell implementation of r required four NMOS

transistors and a capacitor. This circuit configuration, shown in Fig.(2.25) is equivalent

to a current-mode low pass filter. This synapse circuit based on the model of Destexhe

[22] is the first implementation which employs the CMOS current mode log domain

filter in subthreshold CMOS technology.

A synapse implemented with only a few transistors and capacitors was reported by Gor-

don et al. [34]. This idea uses a floating gate MOSFET where its gate was controlled

with biased capacitors and a CMOS inverter. Gordon’s synapse transistor circuit is

shown in Fig.(2.26).

This floating gate MOSFET with a CMOS inverter gave a waveform which fits the bi-

ological synapse model of Rall [35]. Furthermore when the bias potential of this circuit

was properly tuned, its output waveform would match the postsynaptic potential of

the excitatory and inhibitory synapse recorded from the neurons experiment of Wall

et al. [36]. However, this floating gate CMOS circuit is not suitable for implantable

applications. This is because the high current and high voltage properties of this circuit


inVinV

outI

1xI

2xI

bI

outI

M1 M2

1DI2DI

X

2

inV

2

inV

SV

GVBV

DSi

DV

T

gsDS

nU

VVI )(

T

bsDS

nU

VVIn )()1(

dV

gVbVDSi

+n

n 1

n

1

+-

-

1

bV

gV

sV

DSi

bI

outI

2

inV

2

inV

Na

+ c

ha

nn

el

gL

INa IL

EL

Cmem

nI

nn II

Iout

I0

I0IK(Iout1)

K+ channel

I][TI

I0][TI

I0

Iout

Figure 2.25: r implementation with a Bernoulli cell by Lazaridis et al. [33]

might be harmful to organisms.

Another interesting work on a biomimetic synapse is the use of a MOSFET-based mem-

ory device to match the function of a synapse. Yu et al. [37] reported the potential use

of a metal oxide resistive switching memory for this application. This device is made of

Titanium Nitride (TiN), Hafnium Oxide (HfOx), Aluminium Oxide (AlOx) and Plat-

inum (Pt), as the base materials. This non-volatile memory device is a simple capacitor

network which performs an integration to duplicate the function of synapses. The ben-

efit from the synapse implementation on this approach is that it requires comparatively

smaller chip area than the conventional electronic circuit.

From the implementations of the synapse shown earlier in this section, there are no

2.6. Summary 46

inVinV

outI

1xI

2xI

bI

outI

M1 M2

1DI2DI

X

2

inV

2

inV

SV

GVBV

DSi

DV

T

gsDS

nU

VVI )(

T

bsDS

nU

VVIn )()1(

dV

gVbVDSi

+n

n 1

n

1

+-

-

1

bV

gV

sV

DSi

bI

outI

2

inV

2

inV

Na

+ c

ha

nn

el

gL

INa IL

EL

Cmem

nI

nn II

Iout

I0

I0IK(Iout1)

K+ channel

I][TI

I0][TI

I0

Iout

ECa

Vp

Vn

Vtun

Vin

Vout

Figure 2.26: Gordon’s synapse circuit

implementation that has been formulated for a specific receptor type or with actual

neurotransmitter sensing. The integration between an electronic circuit which performs

the chemical synapse function, and a chemical sensor, which has the capability to de-

tect neurotransmitters, will lead to a complete OR a fully-functional bionics chemical

synapse. An integrated implementation of the chemical synapse will be presented later

in Chapter 5.

2.6 Summary

In this chapter, the principle and the biological aspects of neurons were introduced. The

action potential, the signal used for neuron communication, was described. Further-

more, various models of the membrane potential were mathematically explained, such

2.6. Summary 47

as the Hodgkin-Huxley model, the integrate-fire model, the FitzHugh-Nagumo model

and the Morris-Lecar model. The Hodgkin-Huxley model was examined in greater de-

tail, especially the function of the current channels (Na and K) which was also simulated

in MATLAB.

The other main content of this chapter is the function of synapses. The chemical

synapse model by Destexhe was introduced and its simulation results on MATLAB

were shown. Moreover, from the aspect of the bio-inspired circuits, examples of the

silicon implementation of neurons and synapses were reviewed.

References

[1] R. F. Lyon and C. Mead, “An analog electronic cochlea,” Acoustics, Speech and

Signal Processing, IEEE Transactions on, vol. 36, no. 7, pp. 1119–1134, 1988.

[2] V. Chan, S.-C. Liu, and A. van Schaik, “Aer ear: A matched silicon cochlea pair

with address event representation interface,” Circuits and Systems I: Regular Pa-

pers, IEEE Transactions on, vol. 54, no. 1, pp. 48–59, 2007.

[3] B. Wen and K. Boahen, “A silicon cochlea with active coupling,” Biomedical Cir-

cuits and Systems, IEEE Transactions on, vol. 3, no. 6, pp. 444–455, 2009.

[4] J. Costas-Santos, T. Serrano-Gotarredona, R. Serrano-Gotarredona, and

B. Linares-Barranco, “A spatial contrast retina with on-chip calibration for neuro-

morphic spike-based aer vision systems,” Circuits and Systems I: Regular Papers,

IEEE Transactions on, vol. 54, no. 7, pp. 1444–1458, 2007.

[5] R. Serrano-Gotarredona, T. Serrano-Gotarredona, A. Acosta-Jimenez, and

B. Linares-Barranco, “A neuromorphic cortical-layer microchip for spike-based

event processing vision systems,” Circuits and Systems I: Regular Papers, IEEE

Transactions on, vol. 53, no. 12, pp. 2548–2566, 2006.

48

REFERENCES 49

[6] K. A. Zaghloul and K. Boahen, “Optic nerve signals in a neuromorphic chip II:

testing and results,” Biomedical Engineering, IEEE Transactions on, vol. 51, no. 4,

pp. 667–675, 2004.

[7] T. J. Koickal, A. Hamilton, S. L. Tan, J. A. Covington, J. W. Gardner, and T. C.

Pearce, “Analog vlsi circuit implementation of an adaptive neuromorphic olfaction

chip,” Circuits and Systems I: Regular Papers, IEEE Transactions on, vol. 54,

no. 1, pp. 60–73, 2007.

[8] D. Ottoson, Physiology of the Nervous System. Oxford University Press, 1983.

[9] E. Izhikevich, Dynamical Systems in Neuroscience: The Geometry of Excitability

and Bursting (Computational Neuroscience). The MIT Press, 11/01 2006.

[10] R. Plonsey and R. C. Barr, Bioelectricity : A Quantitative Approach. Springer,

2007.

[11] L. Lapicque, “Recherches quantitatives sur l’excitation electrique des nerfs traitee

comme une polarization,” J Physiol Pathol Gen, vol. 9, pp. 620–635, 1907.

[12] A. L. Hodgkin, A. F. Huxley, and B. Katz, “Measurement of current-voltage rela-

tions in the membrane of the giant axon of loligo,” J Physiol, vol. 116, no. 4, pp.

424–448, April 28 1952.

[13] R. FitzHugh, “Impulses and physiological states in theoretical models of nerve

membrane,” Biophysical journal, vol. 1, no. 6, pp. 445–466, 7 1961.

[14] J. Nagumo, S. Arimoto, and S. Yoshizawa, “An active pulse transmission line

simulating nerve axon,” Proceedings of the IRE, vol. 50, no. 10, pp. 2061–2070,

1962.

[15] C. Morris and H. Lecar, “Voltage oscillations in the barnacle giant muscle fiber,”

Biophysical journal, vol. 35, no. 1, pp. 193–213, 7 1981.

REFERENCES 50

[16] A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current

and its application to conduction and excitation in nerve,” J Physiol, vol. 117,

no. 4, pp. 500–544, August 28 1952.

[17] A. L. Hodgkin and A. L. Huxley, “Currents carried by sodium and potassium ions

through the membrane of the giant axon of loligo,” J Physiol, vol. 116, no. 4, pp.

449–472, April 28 1952.

[18] A. Hodgkin and A. Huxley, “The components of membrane conductance in the

giant axon of loligo,” J Physiol, vol. 116, no. 4, pp. 473–496, April 28 1952.

[19] A. L. Hodgkin and A. L. Huxley, “The dual effect of membrane potential on sodium

conductance in the giant axon of loligo,” J Physiol, vol. 116, no. 4, pp. 497–506,

April 28 1952.

[20] A. Destexhe, Z. F. Mainen, and T. J. Sejnowski, “An efficient method for comput-

ing synaptic conductances based on a kinetic model of receptor binding,” Neural

computation, vol. 6, no. 1, pp. 14–18, 1994.

[21] R. B. Northrop, Introduction to Dynamic Modeling of Neuro-Sensory Systems.

CRC Press, 2000.

[22] A. Destexhe, “Synthesis of models for excitable membranes, synaptic transmission

and neuromodulation using a common kinetic formalism,” Journal of computational

neuroscience, vol. 1, no. 3, p. 195, 1994.

[23] W. M. Yamada and R. S. Zucker, “Time course of transmitter release calculated

from simulations of a calcium diffusion model.” Biophys.J., vol. 61, no. 3, pp. 671–

682, March 1 1992.

REFERENCES 51

[24] R. Bertram, Mathematical Models of Synaptic Transmission and Short-Term Plas-

ticity, 1st ed., ser. Tutorials in Mathematical Biosciences II. Berlin / Heidelberg:

Springer, 2005, pp. 173–202.



MIT Press, 1998, pp. 1–26.

[26] C. Mead, “Neuromorphic electronic systems,” Proceedings of the IEEE, vol. 78,

no. 10, pp. 1629–1636, 1990.

[27] M. Mahowald and R. Douglas, “A silicon neuron,” Nature, vol. 354, no. 6354, pp.

515–518, 12/26 1991.

[28] E. M. Drakakis, A. J. Payne, and C. Toumazou, “”Log-domain state-space”: a

systematic transistor-level approach for log-domain filtering,” pp. 290–305, 1999.

[29] C. Toumazou, J. Georgiou, and E. M. Drakakis, “Current-mode analogue cir-

cuit representation of hodgkin and huxley neuron equations,” Electronics Letters,

vol. 34, no. 14, pp. 1376–1377, 1998.

[30] E. Lazaridis and E. M. Drakakis, “Full analogue electronic realisation of the

hodgkin-huxley neuronal dynamics in weak-inversion CMOS,” in Engineering in

Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Con-

ference of the IEEE, 2007, pp. 1200–1203.

[31] E. Farquhar and P. Hasler, “A bio-physically inspired silicon neuron,” Circuits and

Systems I: Regular Papers, IEEE Transactions on, vol. 52, no. 3, pp. 477–488,

2005.

[32] L. Alvado, S. Saghi, J. Tomas, and S. Renaud, “An exponential-decay synapse

integrated circuit for bio-inspired neural networks.” in Computational Methods in

REFERENCES 52

Neural Modeling, ser. Lecture Notes in Computer Science, J. Mira and J. lvarez,

Eds. Springer Berlin / Heidelberg, 2003, vol. 2686, pp. 1040–1040.

[33] E. Lazaridis, E. M. Drakakis, and M. Barahona, “A biomimetic CMOS synapse,”

Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International

Symposium on, p. 4 pp., 2006.

[34] C. Gordon, E. Farquhar, and P. Hasler, “A family of floating-gate adapting synapses

based upon transistor channel models,” in Circuits and Systems, 2004. ISCAS ’04.

Proceedings of the 2004 International Symposium on, vol. 1, 2004, pp. I–317–20

Vol.1.

[35] W. Rall, “Distinguishing theoretical synaptic potentials computed for differ-

ent soma-dendritic distributions of synaptic input,” Journal of Neurophysiology,

vol. 30, no. 5, pp. 1138–1168, September 1 1967.

[36] M. J. Wall, A. Robert, J. R. Howe, and M. M. Usowicz, “The speeding of epsc ki-

netics during maturation of a central synapse,” European Journal of Neuroscience,

vol. 15, no. 5, pp. 785–797, 2002.

[37] S. Yu, Y. Wu, R. Jeyasingh, D. Kuzum, and H. P. Wong, “An electronic synapse

device based on metal oxide resistive switching memory for neuromorphic compu-

tation,” Electron Devices, IEEE Transactions on, vol. 58, no. 8, pp. 2729–2737,

2011.

Chapter 3

Low-gain OTA design

3.1 Introduction

At present, a CMOS analogue circuit operated in weak inversion plays an important role

in biomedical applications. Benefits from this operation range are not only low power

consumption which is the key for implantable systems but also direct analogue com-

putation which required for real time processors. One important element in analogue-

computational systems are Operational Transconductance Amplifiers (OTAs).

A transconductance amplifier is the key element in the silicon implementation of bio-

logical or bio-inspired systems, for example the realisation of the Hodgkin and Huxley

neuron model [1, 2, 3] and the pancreatic cell in [4]. In this thesis, the bio-inspired

circuit of a chemical synapse is implemented according to the Destexhe’s model [5]

where the conductance of each synaptic compartment is 0.1nS. However an operational

transconductance amplifier (OTA) operating in the subthreshold region with a nano-

Ampere range bias current, produces the lowest transconductance gain in the order of

53

3.2. Analysis of differential pair as an OTA 54

nano-Siemens.

To achieve the required level of transconductance gain (0.1nS) while maintaining the

input bias current in the range of nano-Ampere, the OTA design of Sarpeshkar et al.

[6] will be modified by combining it with the OTA linearisation technique of DeWeerth

et al. [7]. The OTA of Sarpeshkar produces a transconductance gain of 2.29nS for a

4nA input bias current, however the modified OTA presented in this thesis can achieve

a transconductance gain of 0.1nS with the same input bias current.

3.2 Analysis of differential pair as an OTA

This section presents the circuit analysis of a normal differential pair OTA. This differ-

ential pair is shown in Fig.(3.1).

bI

outI

M1 M2

1DI 2DIX

2inV

2inV

Figure 3.1: A differential pair transconductance amplifier

3.2. Analysis of differential pair as an OTA 55

At node X, a KCL analysis gives:

ID1 = IOUT + ID2 (3.1)

When this differential pair is biased in the weak inversion region and all the transistors

are perfectly matched, the output current (IOUT ) is equal to:

IOUT = ID1 − ID2

= I0W

L

[exp(

V in

2)− exp(

−V in2

)] (3.2)

The relationship between Ib, ID1 and ID2 is:

Ib = ID1 + ID2

= I0W

L

[exp(

V in

2) + exp(

−V in2

)] (3.3)

The term ( IOUTIb) is a division of eq.(3.2) by (3.3). Thus:

IOUTIb

=I0WL

[exp( Vin

2nUT)− exp( −Vin2nUT

)]

I0WL

[exp( Vin

2nUT) + exp( −Vin2nUT

)]

=exp( Vin

2nUT)− exp( −Vin2nUT

)

exp( Vin2nUT

) + exp( −Vin2nUT)

= tanh(Vin

2nUT)

(3.4)

A Taylor’s series of tanh(x) is:

3.3. Analysis with signal flow graph technique 56

tanh(x) = x− x3

3+

2x5

15− 17x7

315+ ...+ (3.5)

Therefore, the Taylor’s series of tanh( Vin2nUT

) in eq.(3.4) is:

IOUT = Ib

(Vin

2nUT−

( Vin2nUT

)3

3+

2( Vin2nUT

)5

15−

17( Vin2nUT

)7

315+ ...+

)

≈( Ib

2nUT

)Vin

(3.6)

where Ib2nUT

is the transconductance gain of the differential pair.

3.3 Analysis with signal flow graph technique

Alternatively, the analysis of an OTA can be represented by a block diagram of the half

circuit and a signal flow graph as proposed by R. Sarpeshkar et al. [6]. First of all, let

us consider the equation for the drain current of a subthreshold MOSFET:

iDS = I0W

Lexp(

VGSnUT

) exp((n− 1)VBS

nUT) (3.7)

The transconductance of the gate, bulk and source are the derivatives of eq.(3.7):

ggate =∂iDS∂vG

=idsvg

=IDnUT

gbulk =∂iDS∂vB

=idsvb

= (n− 1

nUT)ID

gsource =∂iDS∂vS

=idsvs

=IDUT

(3.8)


Sarpeshkar also recommended that all small signal parameters should be dimensionless

(i = id/ID or v = vd/UT ), therefore id = gdvd = IDvd/nUT which simply is i = v/n.

In this case, n is considered as a dimensionless transconductance. From eq.(3.8), the

dimensionless transconductance of the gate, the bulk and the source are 1/n, (n− 1)/n

and 1, respectively. The small signal equivalent circuit and the signal flow graph of a

MOSFET are shown in Fig.(3.2(b)) and (3.2(c)), respectively.

SV

GVBV

DSi

DV

(a)

T

gsDS

nUVVI )(

T

bsDS

nUVVIn )()1(

dV

gV bVDSi

(b)

+n

n 1

n1

+-

-

1

bV

gV

sV

DSi

(c)

Figure 3.2: (a) A transistor with corresponding voltages and currents. (b) The small sig-nal equivalent circuit for the bulk transistor. (c) The signal flow graph of dimensionlessmodel for the bulk transistor.

An OTA with double source degeneration transistors is shown in Fig.(3.3). The equiv-

alent half circuit and signal flow graph are illustrated in Fig.(3.4(a)) and (3.4(b)), re-

spectively.

The dimensionless transconductance of the gate (1/n2) is attenuated by a feedback fac-

tor of the drain (n1) and the source (n3n4). The overall dimensionless transconductance

for this circuit is given by:

g =1/n2

1 + n1 + (n3n4)(3.9)


bI

outI

2inV

2inV

Figure 3.3: Differential pair as an OTA with double source degeneration

2inV

1

2

3

4

(a)

++

-

1

gV

sV

DSi1n

3n

4n

2

1n

(b)

Figure 3.4: (a) The half equivalent circuit of OTA in Fig.(3.3). (b) The signal flowgraph of Fig.(3.4(a))

Let n1 = np and n2 = n3 = n4 = nn. Thus, eq.(3.9) becomes:


g =1

nn(n2n + np + 1)

(3.10)

g is the dimensionless parameter with Ib2UT

as the multiplication factor to obtain the

actual transconductance. Therefore, the output current from this double source degen-

eration is:

Iout =Ib

2nn(n2n + np + 1)UT

Vin (3.11)

where the tranconductance gain (gm) in this case is Ib2nn(n2

n+np+1)UT. With Ib = 5nA,

nn = 1.3, np = 1.28 and UT = 25.82mV @ 300K, the theoretical gm for this circuit is

18.76nS. This calculation is confirmed by the Cadence simulation result (19.4nS), shown

in Fig.(3.5)

��

��

��

��

��

��

��

�� !��

Figure 3.5: Simulation result for transconductance amplifier in Fig.(3.3)

3.4. Analysis of a bulk driven OTA with source degeneration and bump linearisation60

3.4 Analysis of a bulk driven OTA with source degenera-

tion and bump linearisation

To further reduce the transconductance gain and increase the linear range of the OTA,

R. Sarpeshkar et al. [6] demonstrated a circuit of the differential pair OTA with a bulk

input and bump linearisation [8]. This OTA circuit with two extra diode-connected

PMOS transistors is shown in Fig.(3.6).

outI

2inV

2inV

bI

B1

B2

MN1 MN2

Figure 3.6: Bulk differential pair as an OTA with double source degeneration

First, let’s consider the circuit without considering of the bump linearisation [8] tran-

sistors, shown in the shaded area, the half circuit of this OTA and its signal flow graph

are illustrated in Fig.(3.7(a)) and (3.7(b)), respectively. The dimensionless conductance

(g) for this case is given by:

3.4. Analysis of a bulk driven OTA with source degeneration and bump linearisation61

2inV

1

2

3

4

(a)

inVinV

outI

1xI

2xI

bI

outI

M1 M2

1DI2DI

X

2

inV

2

inV

SV

GVBV

DSi

DV

T

gsDS

nU

VVI )(

T

bsDS

nU

VVIn )()1(

dV

gVbVDSi

+n

n 1

n

1

+-

-

1

bV

gV

sV

DSi

bI

outI

2

inV

2

inV

+

+

-

1

gV

sV

DSi

2

inV

1

2

3

4

1n

3n

4n

2

1

n

2

inV+

+-

-

1

bV

gV

sV

DSi

1

2

3

4

3

3 1

n

n

3

1

n 4n

2n

1n

outI

2

inV

2

inV

bI

B1

B2

MN1 MN2

+

+

-

1

gV

sV

DSi

2

inV

1

2

3

4

1n

3n

4n

2

1

n

inVinV

outI

1xI

2xI

inVinV

outI

1xI

2xI

6.5

6.5

35.0

100

6.5

2.67

35.0

200

6.5

6.5

6.5

6.5

6.5

6.5

6.5

2.67

35.0

100

35.0

100

35.0

100

35.0

100

35.0

100

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

6.5

6.5

6.5

6.5

inVinV

outI

1xI

2xI

6.5

6.5

35.0

100

6.5

2.67

35.0

200

6.5

6.5

6.5

6.5

6.5

6.5

6.5

2.67

35.0

100

35.0

100

35.0

100

35.0

100

35.0

100

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

6.5

6.5

6.5

6.5

N1

P1

N2 N4

N3

N5N6

N7 N8

N9 N10

N11 N12

N13 N14

P2

P3 P4

P5 P6

P7 P8

+

+-

-

1

gV

sV

DSi

2

inV 1

1 1

P

P

n

n

1

1

Pn2Nn

5Pn

3Pn

2

inV

P5

P3

P1

N2

2

inV

N13

N11

N9

N7

+

+

-

1

gV

sV

DSi

11

1

Nn

13Nn

9Nn

7Nn

(b)


g =(n3−1n3

)

1 + n4n3

+ n1n2(3.12)

The subthreshold parameters are: n1 = n2 = n4 = nn and n3 = np. The dimensionless

transconductance in eq.(3.12) is rearranged as:

g =(1− 1

np)

1 + nnnp

+ n2n

(3.13)

Additionally, the extra two transistors (shaded in Fig.(3.6)) can be used to divide the

current from the differential pair and therefore reduce the transconductance gain. This

technique is called the bump linearisation [8]. The ratio between bump transistors (B1

and B2) and transistors (MN1 and MN2), (SB1,2

SMN1,2) or w, determines the transconduc-

tance gain according to eq.(3.14).

3.5. Analysis of double differential pair OTA 62

Iout =sinhx

β + coshx(3.14)

where x = gVinIb2UT

and β = (1 + w2 ). In this thesis, w is set to 12. Thus, eq.(3.14) can be

expanded into a Taylor series as shown in eq.(3.15).

Iout =sinhx

7 + coshx

=x

8+

5x3

384− 13x5

30720− 79x7

2064384+

3407x9

1486356480+ · · ·

≈ x

8

(3.15)

From the approximation in eq.(3.15), the output current of this OTA will be:

Iout =(1− 1

np)Ib

16UT (1 + nnnp

+ n2n)Vin (3.16)

where the transconductance gain (gm) is(1− 1

np)Ib

16UT (1+nnnp

+n2n)

. With Ib = 5nA, nn = 1.3, np

= 1.28 and UT = 25.82mV @ 300K, the theoretical tranconductance gain is 0.724 nS

while the Cadence simulated result was 0.792 nS, shown in Fig.(3.8).

3.5 Analysis of double differential pair OTA

The intention for using the transconductance amplifier in this thesis is to generate

the low synaptic conductance (0.1nS) [5]. This is the main reason to make further

modifications to the OTA, shown in Fig.(3.6), to acquire an even lower transconductance

gain. The technique for this modification was proposed by DeWeerth et al. [7] and

Simoni et al. [1], as shown in Fig.(3.9).


��

��

��

��

��

��

��

��

��

Figure 3.8: Simulation result between output current and differential input voltage ofthe OTA in Fig.(3.6)

This technique employs two differential pair OTAs. The outer differential pair (M5 and

M6) senses the change in the drain potential of the inner differential pair (M1 and M2).

The overall transconductance gain can be analysed by considering the inner differential

pair first.

With respect to the half circuit (only M1 is shown in Fig.(3.10)) of the inner differential

pair, the transconductance gain of the transistor M1 (gm1) is given by:

gm1 =δid1

∂vin

=

(IA2

)nUT

(3.17)

vin = V1 − V2 and IA is the bias current. The change in the M1 drain current (∂id1)

alters the source potential of M3 (V5). The relationship between id1 and v5 is according

to the source tranconductance gs = δidδvs

= IDUT

:


M1 M2

M3 M4

M5

M7 M8

M6

V1 V2

Id1 Id2

Id5 Id6

Iout

IA

IB

V5 V6

Figure 3.9: Variable linear range OTA of S.P. DeWeerth et al.

δv5 =δidgs5

=

(IA2

)δvin

nUT(IA2

)UT

=δvinn

(3.18)

gs5 is the source transconductance of M5. δv5 is conveyed as the input of the outer


M1

M3

V1

Id1

V5

IA2

Figure 3.10: Half circuit of the inner differential pair of Fig.(3.9)

differential pair. For the outer differential pair OTA, the relationship between the

output current (Iout) and V5 − V6 is given by:

Iout =IB

2nUT(V5 − V6) (3.19)

The overall transconductance gain of the circuit in Fig.(3.9) is calculated by substituting

eq.(3.18) into (3.19) and letting δv5 = V5 − V6 and δvin = V1 − V2.

Iout =IB2· 1

nUT︸︷︷︸g1

· 1

n︸︷︷︸g2

·(V1 − V2)

=IB

2n2UT︸︷︷︸gm

(V1 − V2)

(3.20)

From eq.(3.20), it can be concluded that the overall transconductance gain is the prod-

uct of the dimensionless transconductance g1 and g2 of the differential pairs M5,6 and

M1,2, respectively.


Using the similar topology as the circuit shown in Fig.(3.9), the equivalent transconduc-

tance gain of the OTA in Fig.(3.11) can be calculated in the same regime. On the LHS

of Fig.(3.11) is the bulk driven OTA, analysed in the previous section, and on the RHS

(shaded) is the NMOS differential pair OTA with double source degeneration transis-

tors. The other two transistors on top of the RHS differential pair are diode-connected

MOSFETs, which act as the load. The half circuit and signal flow graph diagram of

the shaded OTA are shown in Fig.(3.12(a)) and (3.12(b)), respectively.

inVinV

outI

1xI

2xI

bI

outI

M1 M2

1DI2DI

X

2

inV

2

inV

SV

GVBV

DSi

DV

T

gsDS

nU

VVI )(

T

bsDS

nU

VVIn )()1(

dV

gVbVDSi

+n

n 1

n

1

+-

-

1

bV

gV

sV

DSi

bI

outI

2

inV

2

inV

+

+

-

1

gV

sV

DSi

2

inV

1

2

3

4

1n

3n

4n

2

1

n

2

inV

1

2

3

4

outI

2

inV

2

inV

bI

B1

B2

MN1 MN2

+

+

-

1

gV

sV

DSi

2

inV

1

2

3

4

1n

3n

4n

2

1

n

inVinV

outI

1xI

2xI

inVinV

outI

1xI

2xI

6.5

6.5

35.0

100

6.5

2.67

35.0

200

6.5

6.5

6.5

6.5

6.5

6.5

6.5

2.67

35.0

100

35.0

100

35.0

100

35.0

100

35.0

100

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

6.5

6.5

6.5

6.5

inVinV

outI

1xI

2xI

6.5

6.5

35.0

100

6.5

2.67

35.0

200

6.5

6.5

6.5

6.5

6.5

6.5

6.5

2.67

35.0

100

35.0

100

35.0

100

35.0

100

35.0

100

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

6.5

6.5

6.5

6.5

N1

P1

N2 N4

N3

N5N6

N7 N8

N9 N10

N11 N12

N13 N14

P2

P3 P4

P5 P6

P7 P8

+

+-

-

1

gV

sV

DSi

2

inV 1

1 1

P

P

n

n

1

1

Pn2Nn

5Pn

3Pn

2

inV

P5

P3

P1

N2

2

inV

N13

N11

N9

N7

+

+

-

1

gV

sV

DSi

11

1

Nn

13Nn

9Nn

7Nn

+

+

-

-

1

bV

gV

sV

DSi

3

3 1

n

n

3

1

n 4n

2n

1n

+

+-

-

1

bV

gV

sV

DSi

3

3 1

n

n

3

1

n 4n

2n

1n

Figure 3.11: The double differential pair OTA


2inV

1

2

3

4

(a)

++

-

1

gV

sV

DSi1n

3n

4n

2

1n

(b)


From the signal flow graph in Fig.(3.12(b)), the dimensionless transconductance of the

OTA on the RHS is:

gRHS =1n2

1 + n1 + n3n4(3.21)

where n1, n2, n3 and n4 are the weak inversion slope of the transistor 1, 2, 3 and 4,

respectively. In this case all transistors are NMOS, eq.(3.21) can be rewritten as:

gRHS =1

nn(n2n + nn + 1)

(3.22)

For the complete OTA circuit in Fig.(3.11), the output current is:


Iout =gLHSgRHSIx1

2UT(Vin+ − Vin−)

=(1− 1

np)Ix1

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT(Vin+ − Vin−)

(3.23)

where(1− 1

np)Ix1

nn(1+nnnp

+n2n)(n2

n+nn+1)16UTis the transconductance gain. With Ix1 = 5nA, nn

= 1.3, np = 1.28 and UT = 25.82mV @ 300K, the theoretical gm for this circuit is

1.3968×10−10 S. This is confirmed by the simulation result, shown in Fig.(3.13), where

the obtained tranconductance gain was 1.2615×10−10 S. It should be noted that the

tranconductance gain is independent of Ix2 when the RHS differential pair is operated

in the subthreshold region [7].

��

��

��

�

��

��

��

� ��

Figure 3.13: Simulation result between output current and differential input voltage ofthe OTA in Fig.(3.11)

The important parameters of each transconductance design are summarised in Table

(3.1).

3.6. Summary 69

Table 3.1: Important parameters of each OTAs designOTA topology Linear range Transistor count Theoretical dimensionless gm

A differential pair in Fig.(3.1) 60mV 4 0.769

A differential pair with two source degenera-tion in Fig.(3.3)

260mV 8 0.193

DeWeerth et al. [7] (Fig.(3.9)) 3V 8 0.591

Sarpeshkar et al. [6] (Fig.(3.6)) 3.4V 14 0.00747

The OTA proposed in this work (Fig.(3.11)) 2.5V 22 0.00143

3.6 Summary

In this chapter, the analysis and the design of a low gain transconductance amplifier

have been presented. A traditional differential pair OTA operated in the weak inversion

region was described and analysed. The transconductance gain, a hyperbolic function

is transformed into a Taylor series.

The circuit analysis technique called signal flow graph [6] was explained with a circuit

analysis example. This technique is useful for analysing complex OTA topologies. A

differential pair with a MOSFET body input was introduced to decrease the transcon-

ductance gain and increase the linear range. Furthermore, the analysis of the source

degeneration and the bump linearisation techniques was presented.

At the end of this chapter, the final double differential pair OTA design was analysed.

This OTA combined all the previously mentioned design techniques and was able to

decrease the transconductance gain to the range of sub-nano Siemens (0.1nS for a 3.6nA

bias current).

References

[1] M. F. Simoni, G. S. Cymbalyuk, M. E. Sorensen, R. L. Calabrese, and S. P. De-

Weerth, “A multiconductance silicon neuron with biologically matched dynamics,”

Biomedical Engineering, IEEE Transactions on, vol. 51, no. 2, pp. 342–354, Feb.

2004.

[2] E. M. Drakakis, A. J. Payne, and C. Toumazou, “Log-domain state-space: a sys-

tematic transistor-level approach for log-domain filtering,” Circuits and Systems II:

Analog and Digital Signal Processing, IEEE Transactions on, vol. 46, no. 3, pp.

290–305, 1999.

[3] E. Lazaridis and E. M. Drakakis, “Full analogue electronic realisation of the hodgkin-

huxley neuronal dynamics in weak-inversion cmos,” in Engineering in Medicine and

Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the

IEEE, 2007, pp. 1200–1203.

[4] P. Georgiou and C. Toumazou, “A silicon pancreatic beta cell for diabetes,” Biomed-

ical Circuits and Systems, IEEE Transactions on, vol. 1, no. 1, pp. 39–49, 2007.


sion., 2nd ed., ser. Methods in Neuronal Modeling (2nd ed.). Cambridge, MA: MIT

Press, 1998, pp. 1–26.

70

REFERENCES 71

[6] R. Sarpeshkar, R. F. Lyon, and C. Mead, A low-power wide-linear-range transcon-

ductance amplifier, ser. Neuromorphic Systems Engineering: Neural Networks in

Silicon. Norwell, MA, USA: Kluwer Academic Publishers, 1998, pp. 267–313.

[7] S. P. DeWeerth, G. N. Patel, and M. F. Simoni, “Variable linear-range subthreshold

OTA,” Electronics Letters, vol. 33, no. 15, pp. 1309–1311, 1997.

[8] T. Delbruck, “‘bump’ circuits for computing similarity and dissimilarity of analog

voltages,” in Neural Networks, 1991., IJCNN-91-Seattle International Joint Confer-

ence on, vol. i, 1991, pp. 475–479 vol.1.

Chapter 4

ISFET and Iontophoresis

Technique

4.1 Introduction

The most important element in the bionics chemical synapse is the chemical front-end;

the neurotransmitter sensor. The purpose of this sensor is to function as the receptor

of the chemical synapse i.e. to detect the presence of the neurotransmitter. As the

processing circuit, used to duplicate the biological functionality, will be implemented

using CMOS technology, therefore the sensor for this chemical synapse should also be

integrable on the same platform. The ISFET has demonstrated its ability to function

as the chemical sensor for this work [1].

In this chapter, the principle of an ISFET will be described in terms of its chemical

and mathematical theory. The ISFET’s operation, sensitivity and drift will also be

presented. Additionally, the enzyme-modified ISFET will be introduced as the broad-

72

4.2. ISFET principle 73

specific chemical sensor due to its capability to sense different chemical species. The

glutamate ISFET, that will emulate the function of the AMPA and NMDA receptors

in the bionics chemical synapse, will be studied and implemented from a commercially-

available ISFET.

The second part of this chapter will discuss a fast chemical stimulus technique that

will be used to reproduce the neurotransmitter signal. The required one-millisecond

chemical perturbation is fulfilled by a technique called iontophoresis. Experimental

result shows that this technique can generate a fast chemical stimulus with the desired

time duration.

4.2 ISFET principle

The origin of an ion-sensitive field effect transistor (ISFET) can be traced back to the

1970s with the main contribution made by Piet Bergveld. Initially, the purpose of this

compact solid-state chemical sensor was to act as the probe for the monitoring of ionic

activities in both electrochemical and biological systems [2]. Since then, the research

group at Twente University has published a number of in-depth reports on the charac-

teristics of the ISFET and its applications. The operation and principle of ISFETs and

MOSFETs are similar. An ISFET is a floating gate MOSFET with an extra insulating

membrane. In the case of a MOSFET, its operational regions are determined by its bias

potential at the gate while an ISFET requires a reference electrode (Ag/AgCl) as its

pseudo-gate for biasing. The change in pH alters the threshold voltage of the ISFET,

which can be sensed through the drain current or the gate source potential.

The applications of the ISFET are mainly about pH sensing. However, there have been


extensive use of ISFETs to sense different chemical species, such as glucose [3], urea [4],

glutamate [5], creatine [6], acetylcholine [7], γ-aminobutyric acid [8]. The detection of

these solutions is carried out by the immobilisation of certain enzymes on top of the

gate of the ISFET, called an ENFET. The immobilised enzyme catalyses a chemical

reaction with the interested analyte and the product of this reaction can be H+ or OH–,

altering the local pH at the membrane layer of the ENFET.

4.2.1 ISFET Operation

To understand how an ISFET operates, it is important to analyse the working mecha-

nisms of a MOSFET. The planar cross section of an ISFET is shown in Fig.(4.1). This

diagram is similar to a MOSFET structure with the exception that the gate terminal is

floating and requires a reference electrode for biasing. In the case of the MOSFET, the

current channel through the drain and source is controlled by the gate voltage. In other

words, the gate voltage modulates this channel and the source-drain path is blocked

where there is no gate bias. However, when the voltage bias at the gate is below the

threshold voltage, an exponential relationship between the drain current and the gate

potential is observed. Furthermore, if the gate potential is higher than the threshold

voltage, this relationship becomes either linear in the triode region or square in the

saturation region.

For the ISFET, both the floating gate voltage (biased via the reference electrode) and

the pH of the solution determines the drain current. The effect of the solution’s pH on

the drain current of the ISFET is illustrated in Fig.(4.2). The threshold voltage of an

ISFET (Vth(ISFET )) can be expressed in terms of the threshold voltage of a MOSFET

(Vth(MOSFET )) and the grouping of the pH dependent potentials (Vchem), as shown in

eq.(4.1).


n+n+p+

G

DSB

p-type Si

Insulatingmembrane

SiO2

Figure 4.1: Ion Sensitive Field Effect Transistor

Vth(ISFET ) = Vth(MOSFET ) + Vchem

= Eref − ψs + xsol −φmq

+ Vth(MOSFET )

(4.1)

Vchem = Eref − ψs + xsol −φmq

(4.2)

From eq.(4.1), Eref is the bias potential of the reference electrode, ψs is a pH dependent

chemical potential, xsol is the surface dipole potential of the solution and φmq is the metal

work function. The pH sensitivity of the ISFET or the relationship between Vchem, in

eq.(4.2), and the pH can be explained with the site-binding theory and the double layer

theory.


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Figure 4.2: Drain current vs. reference electrode potential compared to ground fordifferent pH values

4.2.2 ISFET sensitiviy

On top of the ISFET, there is the layer called the passivation layer. Various materials

can be used to construct this layer to provide a different pH sensitivity and dynamic

range [9]. This surface also has the capability to sense positive or negative charges. In

other words, the material’s chemical properties can be acidic, alkali or neutral. In this

case the bare gate material, silicon dioxide (SiO2), will be discussed in the site binding

theory of the ISFET.

The reaction between water and silicon dioxide yields SiO–, SiOH+2 or SiOH. The

neutral binding site (SiOH) can donate or accept protons, as shown in eq.(4.3) and

(4.4) where Ka and Kb are the equilibrium chemical constants.


SiOH −−⇀↽−− SiO− + H+S with Ka =

[SiO−][H+]s[SiOH]

(4.3)

SiOH + H+S−−⇀↽−− SiOH+

2 with Kb =[SiOH+

2 ]

[SiOH][H+]s(4.4)

To find the ability of silicon dioxide to resist a change in pH, the buffer capacity pa-

rameter (β) is defined as:

β =d[B]

dpHs(4.5)

where [B] is the total surface charge which is [SiO–]+[SiOH+2 ] and pHs is the pH at the

SiO2 surface. This total surface charge can be determined from the ratio between the

surface charge density and the charge [10], defined as:

[B] =−σsq

(4.6)

The surface charge density is modelled as two layers which are the Stern inner layer and

the outer diffuse layer [11, 12]. The positive and negative ions in the solution between

the gap of these layers form the equivalent capacitor. The relationship between this

double layer capacitance (Cdl) and σs is:

σs = ψsCdl (4.7)

ψs is the electrical surface potential. Substituting eq.(4.7) into eq.(4.6):


[B] =−1

q(ψsCdl) (4.8)

Substitute eq.(4.8) into eq.(4.5):

β =d(−ψsCdlq )

dpHs

=−Cdlq

dψsdpHs

(4.9)

Rearrange eq.(4.9):

dψsdpHs

=−qβCdl

(4.10)

According to the Boltzmann equation, the concentration of hydrogen ions at the surface

[H+s ] and the bulk concentration of hydrogen ions [H+] is given by:

[H+s ] = [H+] exp(

−qψsKT

) (4.11)

Rearrange eq.(4.11) by substituting [H+s ] = 10-pHs and [H+] = 10-pH:

−qψskT

= ln[10(-pHs+pH)]

ψs = (ln 10)kT

q(pHs − pH)

(4.12)

Differentiate eq.(4.12) with respect to pH:


dψsdpH

= (ln 10)kT

q

(dpHs

dpH− 1

)(4.13)

Substitute dpHsdpH = dpHs

dψs× dψs

dpH into eq.(4.13):

dψsdpH

= 2.3kT

q

[(dpHs

dψs× dψs

dpH

)− 1

]−2.3

kT

q=

dψsdpH

(1− 2.3

kT

q

dpHs

dψs

)dψsdpH

=2.3kTq

2.3kTqdpHsdψs− 1

(4.14)


dψsdpH

=2.3kTq

2.3kTq (−Cdlqβ )− 1

=−2.3kTq

2.3kTq (Cdlqβ ) + 1

= −2.3αUT

(4.15)

where α is the sensitivity coefficient = 1

2.3 kTq

(Cdlqβ

)+1and UT is the thermal voltage = kT

q .

When α reaches unity, this sensitivity will be 59mV/pH at 300K. The pH sensitivity

when α = 1 is defined as the Nernstian sensitivity.

From eq.(4.15), it can be realised that dψs = −2.3αUTdpH or ψs = −2.3αUTpH. With

the substitution of this term to eq.(4.2), the simplified expression [13] for Vchem is:


Vchem = γ +2.3αkT

qpH (4.16)

where γ is the grouping of the non-pH terms. From eq.(4.15), it can be observed that

the buffer capacity for the sensing area of the ISFET is a crucial parameter to deter-

mine its pH sensitivity. A material with a higher buffer capacity provides a greater level

of sensitivity. Amongst the common materials used for the ISFET’s passivation layer,

Ta2O5 gives the highest pH sensitivity owing to its high buffer capacity [14].

A study was conducted to try to provide a pH sensitivity higher than that of the

Nernstian relationship. By adding a chemical, NaF, into the solution [15], the sensitivity

increased to 80-85 mV/pH. However, the dynamic range for this modification is narrow

(in the range of pH 4-6).

4.2.3 Reference electrode

Measurements in electrochemistry made by chemical sensors require a stable potential

as the reference point. This is achieved using a reference electrode, which provides both

a constant potential and an electrical interlink. To comply with this requirement, a

silver-silver chloride common reference electrode is typically used. Fig.(4.3) shows the

physical appearance of this electrode.

To provide a constant potential, the silver wire, coated with silver chloride, will be sub-

merged into a saturated sodium chloride solution (typically 3M). The electrical path is

provided through the porous glass (frit). This glass allows an electrical link to be made

but obstructs any chemical activities between the inside and outside of the electrode.


Y

Ag – AgCl wire

Porous Glass (Frit) Saturate KCl

Figure 4.3: Ag-AgCl reference electrode

Also, the other function of this frit is to ensure that there is no any mix up between

saturated KCl solution inside the reference electrode and outside measured solution.

This separation between the measured solution and the KCl solution in the frit keeps

the potential of the Ag-AgCl electrode constant.

Another type of reference electrode, where there is no liquid junction between the ref-

erence wire and the environmental solution, is a pseudo reference electrode. Without

the reference electrode, the electrical potential of this reference electrode is uncertain.

Practical use of the pseudo reference electrode requires a more complex measurement

system. For example, a research group at Twente University under Bergveld employed

two ISFETs for measurements with a pseudo reference electrode [16]. One was desig-

nated as the measurement ISFET while the other acted as the reference. To obtain

the correct electrochemical signal, differentiation between these two ISFET signals was

required.


Another interesting development in this area is the introduction of a micro reference

electrode. In 2003, Huang et al. fabricated a miniature Ag-AgCl reference electrode

[17]. However, this micro reference electrode cannot be integrated using a standard

CMOS process. Lastly, the integrated reference electrode approach was initiated by

Comte et al. in 1978 [18], where another ISFET (REFET) is used as a reference to a

measurement ISFET. However, it is difficult to fabricate a fully matched REFET and

ISFET.

4.2.4 Drift in ISFET

One of the non-ideal behaviours that can be found in an electrochemical sensor is the

increase or decrease in the measured signal when there is no actual change in the chemi-

cal concentration. This slow and uni-directional change is called drift. Drift is classified

into two categories: short- and long-term. Changes of a few mV per hour when an

ISFET has been in contact with the chemical solution for a few hours are considered as

the short-term drift [19]. A drift is considered to be a long-term drift when an ISFET

has been measuring for at least ten hours; furthermore, this change in the measured

signal could possibly be ten times more than in the short-tem drift case [19].

The method for the deposition of the sensing material on an ISFET was reported as

one of the factors that affect drift in ISFETs. Hammond et al. reported that drift in his

CMOS ISFET is higher than the post-processed ISFET [20]. His report also explained

and compared the physical difference in the material between a CMOS and a non-CMOS

ISFET. The plasma enhanced chemical vapour (PECVD) deposition method used in

CMOS ISFET is a lower temperature process, which produces non-uniform crystals or

a polycrystalline. On the other hand, a non-CMOS ISFET’s sensing membrane is grown

in a low pressure/high temperature condition which yields a single crystal.

4.3. Enzyme-Immobilised ISFET 83

There have been many attempts to compensate for drift in an ISFET. All of the drift

reduction schemes employed additional electronic circuits to counteract the drift in the

signal. This method requires an accurate drift model of the ISFET. However, the drift

effect in an ISFET can be neglected if the measurement time duration is less than a

minute. This means that a millisecond range chemical perturbation in the experiment

of this work will not be affected by both long or short term drift.

4.3 Enzyme-Immobilised ISFET

Typically, an ISFET is designed for pH sensing or the detection of a change in hydrogen

concentration. However, the function of an ISFET is not only limited to pH sensing.

There have been many research works that applied ISFETs as a broad-specific chemical

sensor, through extra modifications and post-processesing. An ISFET deposited with

an ion-selective membrane, which makes it sensitive to a specific ion, is classified as a

CHEMFET.

As mentioned earlier in section 4.2, an ISFET with an extra enzyme layer on top of

the sensing membrane is called EnFETs. The function of this enzyme is to catalyse

the chemical reaction to either yield extra protons or electrons. This means that the

EnFET measures the ions that are generated as a by-product of the hydrolysis reaction.

The first EnFET publication was made by Caras et al. in 1980 [21], where he proposed

an EnFET for sensing penicillin. Their penicillin-FET was coated with penicillinase.

An example of an EnFET that will be described here is the EnFET for detecting urea

[22].


Urea + 3 H2OUREASE−−−−−−→ CO2 + 2 NH+

4 + 2 OH− (4.17)

From eq.(4.17), when urea is hydrolysed with urease as the catalyst, this chemical re-

action will give extra OH– ions. The increase in OH– ions leads to a pH change which

can be sensed by the EnFET. Table (4.1) summarises the common analytes that can be

sensed with an ISFET.

Table 4.1: Common analytes and immobilised enzymes used in EnFETAnalyte Immobilised enzyme Local pH change Reference

Penicillin Penicillinase decrease [21]Glucose Glucose oxidase decrease [23]Lactate Lactate oxidase decrease [24]

Urea Urease increase [22]Creatinine Creatinine deiminase decrease [25]Glutamate Glutamate oxidase decrease [26]

γ-Aminobutyric acid GABA oxidase decrease [27]Acetylcholine Acetylcholine esterase decrease [28]

Caras also reported an interesting observation regarding the relationship between the

penicillin-FET’s linear range and sensitivity with the buffer capacity of the analyte. In

this experiment, it was observed that the Penicillin-ISFET tested in a higher concen-

tration buffer solution had a broader linear range and a lower sensitivity. In contrast,

the same EnFET gave a shorter linear range and a higher sensitivity when operated in

a lower concentration buffer solution.

4.3.1 Glutamate ISFET

According to the Destexhe’s chemical synapse model [29] on the AMPA and NMDA

receptors, the bionics version of these receptors requires a glutamate sensor as the


chemical input. The glutamate ISFETs used in this work are the Sentron ISFETs

(Sentron BV, the Netherlands) with glutamate oxidase (GLOD) immobilisation. The

chemical reaction of glutamate, catalysed by GLOD, is expressed as:

Glutamate + O2 + H2OGLOD−−−−→ 2− oxoglutarate + NH+

4 + H2O2

The procedures for this immobilisation follow Braeken et al.’s work [26]. The procedures

undertaken to achieved GLOD immobilisation are as follows:

� The ISFET’s surface was cleaned with a 3:1 solution of sulfuric acid and hydrogen

peroxide for 15 minutes.

� The cleaned ISFET was further treated in a UV/ozone machine for 15 minutes.

� After the UV/Ozone treatment, the ISFET was rinsed with ethanol.

� The ISFET was heated at 110◦C for 30 minutes on a hot plate.

� The ISFET was immersed in a 1:1 solution of Poly-l-lysine (PLL) solution (PLL

4mg/mL in 10mM borate buffer pH8) and sodium cynaoborohydride (NaCNBH3)

for 30 minutes.

� The ISFET was further immersed in a 1:1 V/V solution of glutaraldehyde solution

(GA) and NaCNBH3 for 30 minutes.

� GLOD coupling on the ISFETs surface was accomplished by using a pipette to

drop a 300µg/mL GLOD in PBS solution on the ISFET’s surface. The time

duration for this process was overnight.

� The overnight GLOD-coupled ISFETs was further treated with CNBH for 30

minutes.


The second treatment of the ISFETs with CNBH after GLOD coupling is vital for a

robust linkage of GLOD [26]. Also, it is advised that these glutamate ISFETs should

be kept at 4◦C with a Tris buffer (150mM) inside a light tight container [26].

For the bionics chemical synapse, a linear relationship between the ion concentration and

the output signal is required. However, a typical voltage-mode ISFET readout circuit

[30] has a logarithmic relationship. Five different concentration of HCl (0.5, 1, 1.5, 2

and 2.5mM) were tested with this ISFET readout circuit. The obtained calibration

curve, shown in Table 4.2 and Fig.(3.6), exhibits a logarithmic relationship.

Table 4.2: Data of the measured results for different HCl concentration of 0.5, 1, 1.5, 2and 2.5mM from a voltage-mode readout circuit [30]

HCL concentration (mM) Output voltage (mV)

0.5 -789.11 -770.5

1.5 -763.32 -757.2

2.5 -748.5

This readout circuit was operated in a dual supply ±6V and the reference electrode was

biased at 0V. This logarithmic curve can be linearised with the H-cell current mode

readout circuit, shown in Fig.(4.5), proposed by Shepherd et al. [31].

In Fig.(4.5), the ISFET current (IISFET ) is the square root function of the ions con-

centration. The relationship between IISFET and I1 [31] is:

IISFET = I1[IONS]0.5e−γnUT e

−VrefUT (4.18)

The translinear loop on transistors P1, P2, P3 and P4 functions as a current squarer.


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Figure 4.4: Measured results for different HCl concentration of 0.5, 1, 1.5, 2 and 2.5mMfrom a voltage-mode readout circuit [30]

The output current of this readout circuit (Iout) when I1 = I2 is:

Iout = I1[IONS]e2γnUT e

−2VrefUT (4.19)

where γ is the grouping of all pH-independent chemical potentials, n is the subthreshold

parameter of a MOSFET, Vref is the DC potential used for biasing the transistor N1

and the ISFET N2 to operate in weak inversion, [IONS] is the concentration of the

interested solution and UT is the thermal voltage. Different solutions of HCl as presented

in Table 4.2 were measured using the ISFET with this current mode readout circuit.

The measured results are shown in Table 4.3 and Fig.(4.6).

The trend line in Fig.(4.6) shows a linear relationship between the concentration of HCl

and the output current. This trend line has an r-square parameter of 0.987. The ISFET

(N2) and the transistors (P1, P2 and P4) were biased at 0.44V.


Y

refV

1I

2I

outI

N1 N2

P1

P2

P3

P4

ISFETI

Figure 4.5: Current mode ISFET readout circuit which exhibits a linear relationshipbetween the output current and the concentration of analyte

When this readout circuit is applied to the glutamate ISFET measuring five different

glutamate concentration solutions (0.5, 1, 1.5, 2 and 2.5mM), a linear relationship be-

tween the glutamate concentration and the output current was also obtained, as shown

in Table 4.4 and Fig.(4.7). The glutamate solutions were prepared from L-glutamatic

acid (Sigma, UK) in a phosphate buffer saline solution (PBS, 10mM, pH 7).

The R2 of the trend line curve in Fig.(4.7) is 0.9957, indicating a good fit to the data.

It was observed that the glutamate ISFET required a higher voltage bias than an or-

dinary ISFET. This phenomena can be explained by the extra glutamate oxidase layer


Table 4.3: Data of the measured results for different HCl concentration of 0.5, 1, 1.5, 2and 2.5mM from the current mode readout circuit in [31] when Vref = 0.44V

HCL concentration (mM) Output current (nA)

0.5 14.121 31.23

1.5 44.522 56.53

2.5 63.66

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Figure 4.6: Measured results for different HCL concentration of 0.5, 1, 1.5, 2 and 2.5mMfrom the current mode readout circuit in [31] when Vref = 0.44V

on top of the ISFET’s sensing membrane, which forms an extra equivalent capacitor.

This capacitor divides the bias potential from the reference electrode to the source of

the glutamate ISFET.

From the measured results in Table. 4.4, it would be useful if the value of γ in eq.(4.19)

can be extracted for further use. Eq.(4.19) can be rearranged to give:


Table 4.4: Data of the measured results for different glutamate concentration of 0.5, 1,1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.26V

Glutamate concentration (mM) Output current (nA)

0.5 51.191 56.29

1.5 60.052 63.59

2.5 67.71

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Figure 4.7: Measured results for different glutamate concentration of 0.5, 1, 1.5, 2 and2.5mM from the current mode readout circuit in [31] when Vref = 0.26V

γ =nUT

2ln

(Iout

[IONS]× 1

I1e−2VrefUT

)(4.20)

Iout[IONS] is the sensitivity of the current mode readout circuit (nA/mM). To verify the

calculated γ, another set of measured results with a different bias voltage (Vref ) were

gathered. This is shown in Table 4.5 and Fig.(4.8).

From Fig.(4.8), the R2 is 0.9947. The sensitivities ( Iout[IONS]) are 142.04nA/mM and


Table 4.5: Data of the measured results for different glutamate concentration of 0.5, 1,1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.21V

Glutamate concentration (mM) Output current (nA)

0.5 403.591 472.89

1.5 541.252 601.15

2.5 694.56

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Figure 4.8: Measured results for different glutamate concentration of 0.5, 1, 1.5, 2 and2.5mM from the current mode readout circuit in [31] when Vref = 0.21V

8.068nA/mM when Vref are 0.21V and 0.26V, respectively. Using eq.(4.20), the gamma

parameters are 400.223 and 416.822 for Vref = 0.21V and 0.26V, respectively. It should

be noted here that there were five ISFETs which immobilised in this work. Each gluta-

mate ISFET was tested to find its calibration curve. The results shown in this section

were extracted from the highest sensitivity glutamate ISFET among these five gluta-

mate ISFETs.

4.4. Coulometric titration 92

The combination of the modified ISFET, for glutamate sensing, and the current-mode

readout circuit, operated in the weak inversion region, exhibit excellent linearity between

the output current and the concentration of glutamate. Furthermore, the sensitivity of

this sensor system is tunable, as confirmed by the measured results. Therefore, it can be

concluded that this is the first linear and sensitivity-controllable electrochemical sensor

for glutamate.

4.4 Coulometric titration

The research group at Twente University reported a method that can be used to create

a fast ion concentration change suitable for ISFET sensing in the 1980s. The flow injec-

tion, the first technique, was implemented with a high speed pump and valve, where two

different pH solutions were pumped to the sensing membrane of the ISFET. This report

[32] indicated that both the ascent rate of the pH gradient and the buffer capacity have

an influence on the response time.

Another approach to create a rapid ionic perturbation, reported by Bergveld’s group,

is the coulometric titration technique. This technique requires two electrodes and a

current source. The generating electrode (anode) produces H+ ions while the counter

electrode (cathode) yields OH– ions. The chemical reaction, the oxidation and reduction

of water molecule, at these two electrodes and the diagram of this technique are shown

in eq.(4.21) and Fig.(4.9), respectively.

At anode (generating electrode) : H2O→ 2 H+ + 2 e− +1

2O2

At cathode (counter electrode) : H2O + e− → OH− +1

2H2

(4.21)


Generating electrode Counter electrode

Current source

H+

OH-

OH-

OH-

OH-

H+ H

+

H+

Figure 4.9: Diagram of coulometric titration

For a constant applied current (I), the concentration (C) of the species added into the

solution by the generator will be directly proportion to the applied period (t). The

coulometric relationship [16] is given by eq.(4.22).

C =It

nFA(4.22)

where n is the number of moles of e– in the reaction, F is the Faraday’s constant, A is

the surface area of the generator. When H+ ion generation at the anode and the OH–

ions generate at the cathode occur in a separated system with no mixing and titration

between the generated H+ and OH– ion generation, the change in H+ ions observed by

the ISFET is only influenced by the H+ ion concentration produced at the generator.

As a result, a larger signal can be observed by the ISFET after a longer generation

period.

As diffusion is the main mechanism for the movement of the H+ and OH– ions in the

bulk solution, the maximum time response and the thickness of a diffusion layer can be

estimated. The maximum time response determines how fast the ions can diffuse to a


certain distance (i.e. the thickness of a diffusion layer). The relationship between time

and distance of the diffusion phenomena [11] can be described according to the eq.(4.23).

L =√

2Dt (4.23)

where L is the thickness of a diffusion layer, t is time response and D is the diffusion

coefficient. In the case of the hydrogen ions, the diffusion coefficient (DH+) is 9.3×10−9

m/s2 [33]. If the required minimum response time is assumed as 1ms, the maximum

distance between the generated electrode and the sensor should be less than or equal to

4.31 µm. The significance of this equation is that it can be used as an estimate of the

maximum time response when the distance is known.

Furthermore, other chemical species can also be produced using the coulometric tech-

nique. For instance, Ag+ can be induced by using a silver wire as the generating

electrode, and similarly, by using a mercury wire as the generating electrode, Hg2+ can

be produced.

It should be noted that the coulometric titration technique can only be implemented

where the titrant is in the form of a solid metal. This is because electrical current is

central to this ion generation process. If the interested titrant is not in the rigid form

and has no electrical conductivity, this technique will not be applicable.

4.5. Iontophoresis method 95

4.5 Iontophoresis method

In the case where the titrant is not in the solid form and has no electrical conductivity,

iontophoresis is an alternative approach to create an ion flow in a similar way to the

coulometric titration method. The iontophoresis technique has been extensively used

to conduct experiments in neurological studies especially in the delivery of neuroactive

substances. The diagram of this technique is shown in Fig.(4.10).

Figure 4.10: Diagram of iontophoresis

The ejection of ions in this technique is controlled by the applied current via the current

source. A positive current (i.e. electrode 1 has a positive potential) will repel the posi-


tive ions out of the micropipette while the negative ions will be attracted to electrode 1.

In the case shown in Fig.(4.10), a positive current is applied to the HCl solution which

causes H+ ions to flow out of the micropipette. The diameter of the micropipette tip

should be around 1 µm or less to decrease the probability of the incontinent diffusion

and to achieve a low tip potential [34].

As the implementation of a silicon chemical synapse [35] requires a fast neurotransmit-

ter test stimulus, the iontophoresis technique can be used to emulate the ion flow when

the neurotransmitter is released, for instance: the flow of glutamate can be achieved

by using sodium glutamate as the solution in the micropipette with a negative applied

current.

The quantity of ions released from the tip of the micropipette can be described by Fick’s

law of diffusion, shown in eq.(4.24).

C(r, t) = C(0) erfc

(r

2√Dt

)(4.24)

where C is the concentration of the ejected ions, r is the distance, t is time duration,

C(0) is the concentration at the position of x = 0 and D is the diffusion coefficient.

C(0) in eq.(4.24) can be modified [36, 37] to give:

C(r, t) =in

F4πDrerfc

(r

2√Dt

)(4.25)


where i is an electrical current, F is the Faraday constant and n is the transport num-

bers. To simplify the complement error function (erfc), let’s consider the transformation

of the complement error function shown in eq.(4.26)

erfc(a) =e−a

2

a√π

∞∑n=0

(−1)n · (2n− 1)!!

(2a2)n(4.26)

Substitute a = r2√Dt

into eq.(4.26):

erfc

(r

2√Dt

)=

e−(

r

2√Dt

)2(r

2√Dt

)√π

∞∑n=0

(−1)n · (2n− 1)!!(2(

r2√Dt

)2)n (4.27)


C(r, t) =in

F4πDr

e−(

r

2√Dt

)2(r

2√Dt

)√π

∞∑n=0

(−1)n · (2n− 1)!!(2(

r2√Dt

)2)n (4.28)

Rearrange eq.(4.28):

C(r, t) =

in2Fr2

√t

Dπ3

exp(r2

4Dt

) ∞∑n=0

(−1)n · (2n− 1)!!(2(

r2√Dt

)2)n (4.29)

Considering only the coefficient terms in eq.(4.29)


C(r, t) ∝in

2Fr2

√t

Dπ3

exp(r2

4Dt

) (4.30)

From the eq.(4.30), it can be observed that the concentration (C) is directly proportional

to the current (i) and time duration (t), while the distance (r) is inversely proportional

to the concentration (C).

Likewise, by only considering eq.(4.25) and neglecting the complementary error func-

tion (erfc), the transport number (n) can be related to the time duration of the injected

current (t). The transport numbers (or transference numbers) of the ion x is defined

as the fraction of the ion x’s conductivity over the whole conductivity [11], shown in

eq.(4.31).

nx =|zx|uxCx∑j

|zj |ujCj(4.31)

where nx is the transport number of the ion x, zx is the magnitude of charge of the

ion x, ux is the mobility of the ion x, and Cx is the concentration of the ion x. From

eq.(4.31), the transport number is directly proportional to the magnitude of the charges.

nx ∝ zx (4.32)

Additionally, the definition of the charges is described as a product of the current (I)

and time (t).

4.6. Experimental results on Iontophoresis 99

zx = It (4.33)

From eq.(4.32) and eq.(4.33), it can be concluded that the transport number is directly

proportional to time.

nx ∝ zx ∝ t (4.34)

The neurotransmitter signal in Destexhe’s chemical synapse model [29] is expressed as a

1ms duration pulse with a 1mM amplitude. To create an ionic perturbation within the

millisecond range, a calculation based on the iontophoresis technique [37] is carried out.

Recalling that the amount of ion concentration ejected at the tip of the micropipette is

given by eq.(4.25). Eq.(4.25) can be re-arranged to give:

i =4FDπr[IONS]

n× erfc( r2√Dt

)(4.35)

Therefore, the required current (i) to generate a 1mM glutamate ([IONS]) change with

a 1ms time duration (t) where the diffusion of glutamate (D) = 2.5×10−10 m2/s [38],

the distance (r) = 2µm, the transport number of glutamate (n) = 0.4 [38] and the

Faraday’s constant (F ) = 96485.3399 C/mol, is 0.4µA.

4.6 Experimental results on Iontophoresis

The system used for the iontophoresis experiment is shown in Fig.(4.11). This system

consists of three main parts: the glass micropipette with 1µm diameter (from World

Precision Instrument Ltd), two platinum electrodes (Pt1 and Pt2), the AC current


source (Keithley model 6221), the ISFET from Sentron Europe B.V. and the opamp

driven readout circuit [39]. In the experiment, the 3M HCl solution was put into the

micropipette for H+ perturbation. The bulk solution used in this case was the Phos-

phate Buffer Saline (PBS).

Generating electrode Counter electrode

Current source

OH-

OH-

OH- OH

-

H+ H

+

+

-

HCl solution

Micropipette

H+

Cl-

H+

H+

H+

H+

H+

H+

H+

H+

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

H+

H+

Current source

Electrode1

Electrode2

Chemical Sensor

+

-

HCl solution

Micropipette

H+

Cl-

H+

H+

H+

H+

H+

H+

H+

H+

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

H+

H+

Keithley 6221 AC

Current source

PT1

PT2

Sentron ISFET

G1G2

Reference

electrode

Figure 4.11: System used for iontophoresis experiment

As there was no stirring in this experiment, the convection effect would not influence the

movement of the ejected ions. The change in proton concentration measured through the

ISFET and the readout circuit was a local ion change only; and diffusion was assumed

as the only contribution to the ion concentration fluctuation. The distance between

the ISFET and the tip of micropipette is in order of 1µm; this was controlled by the

micromanipulator. The current source was kept floating to separate the ground of the

current source from the ground of the readout circuit. This floating current source also

ensured that there was no leakage current through the reference electrode.


The first part of this experiment was to determine the relationship between the current

amplitude and the concentration change. A current pulse signal with a 1ms pulse width

was used with the amplitude set at -1µA, +0.6µA and +1.0µA. The result from this

experiment is shown in Fig.(4.12).

Figure 4.12: Measured result for three different injected amplitudes at 1µm distancebetween the micropipette tip and the ISFET’s surface (insert is a ’Zoom in’ of oneperiod of the measured result)

It can be observed from the result in Fig.(4.12) that the greater the amplitude of the

current injected (i), the larger the ion concentration (C) sensed by the ISFET, which is

consistent with eq.(4.30). The negative current test at -1µA amplitude, as expected, did

not produce a response. This is correct because the ISFET is only sensitive to changes

in proton or H+ ions and not Cl– ions.


A 1µA current signal with three different pulse widths (10ms, 1ms and 0.1ms) were

used as the input signal for the second experiment. The result is shown in Fig.(4.13).

Figure 4.13: Measured result for three different current pulse widths at a fixed injectedamplitude of 1uA and a 1µm distance between the micropipette tip and the ISFET’ssurface (insert is a ’Zoom in’ of one period of the measured result)

It can be seen that the ISFET responded to a 1ms pulse but it could not detect a

0.1ms pulse. With a longer injection time, a larger response was obtained, which agrees

with the eq.(4.30). Satisfactory repeatability of the response was observed in successive

proton injections. From the measured results shown earlier, it can be concluded that

this experiment is the first iontophoresis technique to create and verify a millisecond

H+ perturbation on the ISFET.

4.7. Summary 103

4.7 Summary

This chapter has presented the basic concepts of an ISFET such as its operation, sen-

sitivity, which was explained by chemical theories, and drift, one of its imperfections.

The ISFET’s ability to detect different chemical species by modifying the sensing area,

an ENFET, has been described. Furthermore, the modification procedures to produce

the glutamate ISFET has been given. The non-linear relationship between the concen-

tration of hydrogen ions and the output signal of the traditional voltage-mode readout

circuit [30] have been discussed in this chapter. The required linear relationship between

the ion concentration and the output signal can be achieved with a recent current-mode

readout circuit [31].

The later sections of this chapter examined, in particular, the iontophoresis chemical

perturbation technique for the generation of the neurotransmitter test signal in the

Destexhe’s chemical synapse. From the experimental results of this technique, a one

millisecond signal of [H+] ions could be detected and verified with an ISFET. With the

validity of this technique confirmed, iontophoresis can be employed to simulate a fast

chemical stimulus that is representative of the required neurotransmitter signal [29].

References

[1] B. Palan, K. Roubik, M. Husak, and B. Courtois, “CMOS ISFET-based structures

for biomedical applications,” in Microtechnologies in Medicine and Biology, 1st

Annual International, Conference On. 2000, 2000, pp. 502–506.

[2] P. Bergveld, “Development of an ion-sensitive solid-state device for neurophysiolog-

ical measurements,” Biomedical Engineering, IEEE Transactions on, vol. BME-17,

no. 1, pp. 70–71, 1970.

[3] Y.-Q. Zhang, J. Zhu, and R.-A. Gu, “Improved biosensor for glucose based on

glucose oxidase-immobilized silk fibroin membrane,” Applied Biochemistry and

Biotechnology, vol. 75, pp. 215–233, 1998.

[4] C. Eggenstein, M. Borchardt, C. Diekmann, B. Grndig, C. Dumschat, K. Cam-

mann, M. Knoll, and F. Spener, “A disposable biosensor for urea determination in

blood based on an ammonium-sensitive transducer,” Biosensors and Bioelectronics,

vol. 14, no. 1, pp. 33–41, 1 1999.

[5] Q. Li, S. Zhang, and J. Yu, “Immobilization of l-glutamate oxidase and peroxidase

for glutamate determination in flow injection analysis system,” Applied Biochem-

istry and Biotechnology, vol. 59, pp. 53–61, 1996.

104

REFERENCES 105

[6] H. Kinoshita, M. Torimura, K. Kano, and T. Ikeda, “Peroxidase-based ampero-

metric sensor of hydrogen peroxide generated in oxidase reaction: Application to

creatinine and creatine assay,” Electroanalysis, vol. 9, no. 16, pp. 1234–1238, 1997.

[7] T. Horiuchi, K. Torimitsu, K. Yamamoto, and O. Niwa, “On-line flow sensor for

measuring acetylcholine combined with microdialysis sampling probe,” Electroanal-

ysis, vol. 9, no. 12, pp. 912–916, 1997.

[8] N. Osamu, K. Ryoji, H. Tsutomu, and T. Keiichi, “Small-volume on-line sensor for

continuous measurement of γ-aminobutyric acid,” Analytical Chemistry, vol. 70,

no. 1, pp. 89–93, 1998.

[9] B. Palan, “Design of low noise ph-isfet microsensors and integrated suspended in-

ductors with increased quality factor q,” Ph.D. dissertation, Institut Polytechnique

de Grenoble, 2001.

[10] P. Bergveld, R. E. G. van Hal, and J. C. T. Eijkel, “The remarkable similarity

between the acid-base properties of ISFETs and proteins and the consequences for

the design of isfet biosensors,” Biosensors and Bioelectronics, vol. 10, no. 5, pp.

405–414, 1995.

[11] A. J. Bard and L. R. Faulkner, Electrochemical methods : fundamentals and appli-

cations. New York: Wiley, 2001.

[12] D. E. Yates, S. Levine, and T. W. Healy, “Site-binding model of the electrical double

layer at the oxide/water interface,” J. Chem. Soc., Faraday Trans. 1, vol. 70, pp.

1807–1818, 1974.

[13] L. Shepherd and C. Toumazou, “Weak inversion ISFETs for ultra-low power bio-

chemical sensing and real-time analysis,” Sensors and Actuators B: Chemical, vol.

107, no. 1, pp. 468–473, 5/27 2005.

REFERENCES 106

[14] P. Bergveld, “Thirty years of isfetology: What happened in the past 30 years

and what may happen in the next 30 years,” Sensors and Actuators B: Chemical,

vol. 88, no. 1, pp. 1–20, 1/1 2003.

[15] A. S. Poghossian, “The super-nernstian ph sensitivity of Ta2O5 - gate ISFETs,”

Sensors and Actuators B: Chemical, vol. 7, no. 1-3, pp. 367–370, 3 1992.

[16] B. van der Schoot and P. Bergveld, “An ISFET-based microlitre titrator: integra-

tion of a chemical sensoractuator system,” Sensors and Actuators, vol. 8, no. 1, pp.

11–22, 9 1985.

[17] I.-Y. Huang, R.-S. Huang, and L.-H. Lo, “Improvement of integrated Ag/AgCl thin-

film electrodes by KCl-gel coating for ISFET applications,” Sensors and Actuators

B: Chemical, vol. 94, no. 1, pp. 53–64, 8/15 2003.

[18] P. A. Comte and J. Janata, “A field effect transistor as a solid-state reference

electrode,” Analytica Chimica Acta, vol. 101, no. 2, pp. 247–252, 11/1 1978.

[19] S. Jamasb, S. D. Collins, and R. L. Smith, “Correction of instability in ion-selective

field effect transistors (ISFETs) for accurate continuous monitoring of pH,” in

Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual

International Conference of the IEEE, vol. 5, 1997, pp. 2337–2340 vol.5.

[20] P. A. Hammond and D. R. S. Cumming, “Performance and system-on-chip inte-

gration of an unmodified CMOS ISFET,” Sensors and Actuators B: Chemical, vol.

111-112, pp. 254–258, 11/11 2005.

[21] S. Caras and J. Janata, “Field effect transistor sensitive to penicillin,” Analytical

Chemistry, vol. 52, no. 12, pp. 1935–1937, 1980.

[22] D. G. Pijanowska and W. Torbicz, “pH-ISFET based urea biosensor,” Sensors and

Actuators B: Chemical, vol. 44, no. 1-3, pp. 370–376, 10 1997.

REFERENCES 107

[23] C.-H. Lee, H.-I. Seo, Y.-C. Lee, B.-W. Cho, H. Jeong, and B.-K. Sohn, “All solid

type ISFET glucose sensor with fast response and high sensitivity characteristics,”

Sensors and Actuators B: Chemical, vol. 64, no. 1-3, pp. 37–41, 6/10 2000.

[24] J.-J. Xu, W. Zhao, X.-L. Luo, and H.-Y. Chen, “A sensitive biosensor for lactate

based on layer-by-layer assembling MnO2 nanoparticles and lactate oxidase on ion-

sensitive field-effect transistors,” Chem. Commun., pp. 792–794, 2005.

[25] A. P. Soldatkin, J. Montoriol, W. Sant, C. Martelet, and N. Jaffrezic-Renault, “Cre-

atinine sensitive biosensor based on ISFETs and creatinine deiminase immobilised

in bsa membrane,” Talanta, vol. 58, no. 2, pp. 351–357, 8/23 2002.

[26] D. Braeken, D. R. Rand, A. Andrei, R. Huys, M. E. Spira, S. Yitzchaik, J. Shappir,

G. Borghs, G. Callewaert, and C. Bartic, “Glutamate sensing with enzyme-modified

floating-gate field effect transistors,” Biosensors and Bioelectronics, vol. 24, no. 8,

pp. 2384–2389, 4/15 2009.

[27] A. Yamamura, Y. Kimura, S. Tamai, and K. Matsumoto, “Gamma-aminobutyric

acid (GABA) sensor using GABA oxidase from penicillium sp. kait-m-117,” ECS

Meeting Abstracts, vol. 802, no. 46, pp. 2832–2832, 2008.

[28] A. Hai, D. Ben-Haim, N. Korbakov, A. Cohen, J. Shappir, R. Oren, M. E. Spira,

and S. Yitzchaik, “AcetylcholinesteraseISFET based system for the detection of

acetylcholine and acetylcholinesterase inhibitors,” Biosensors and Bioelectronics,

vol. 22, no. 5, pp. 605–612, 12/15 2006.



MIT Press, 1998, pp. 1–26.

REFERENCES 108



pp. 1499–1508, 1980.



on, vol. 52, no. 12, pp. 2614–2619, 2005.

[32] B. van der Schoot, P. Bergveld, M. Bos, and L. J. Bousse, “The ISFET in analytical

chemistry,” Sensors and Actuators, vol. 4, pp. 267–272, 1983.

[33] J. C. van Kerkhof, W. Olthuis, P. Bergveld, and M. Bos, “Tungsten trioxide (WO3)

as an actuator electrode material for ISFET-based coulometric sensor-actuator sys-

tems,” Sensors and Actuators B: Chemical, vol. 3, no. 2, pp. 129–138, 2 1991.

[34] U. Windhorst and H. Johansson, Modern techniques in neuroscience research.

Berlin; New York: Springer, 1999.

[35] S. Thanapitak and C. Toumazou, “Towards a bionic chemical synapse,” in Circuits

and Systems, 2009. ISCAS 2009. IEEE International Symposium on, 2009, pp.

677–680.

[36] H. D. Lux and E. Neher, “The equilibration time course of [K+]0 in cat cortex,”

Experimental Brain Research, vol. 17, pp. 190–205, 1973.

[37] E. Neher and H. D. Lux, “Rapid changes of potassium concentration at the outer

surface of exposed single neurons during membrane current flow,” The Journal of

general physiology, vol. 61, no. 3, pp. 385–399, March 01 1973.

[38] A. Herz, W. Zieglgnsberger, and G. Frber, “Microelectrophoretic studies concern-

ing the spread of glutamic acid and GABA in brain tissue,” Experimental brain

research, vol. 9, no. 3, p. 221, 1969.

REFERENCES 109

[39] H. Nakajima, M. Esashi, and T. Matsuo, “The pH-response of organic gate ISFETs

and the influence of macro-molecule adsorption,” J. Chem. Soc. Jpn., vol. 10, pp.

1499–1508, 1980.

Chapter 5

Bio-inspired Chemical Synapse

5.1 Introduction

Electronic circuits that can mimic the models of a neuron’s membrane potential are

well-established in the field of biomimetic systems. The objective of these implemen-

tations is to create electronic circuits that behave in the same way as a living neuron.

Since the emergence of neuromorphic engineering in the 1980s, there has been an exten-

sive amount of studies and reports on the implementation of neuronal models in silicon

integrated circuit. Examples of this are the conductance-based model implementation

(Hodgkin and Huxley model) in [1], the CA3 neuron model on the hippocampal system

[2] and the Beta cell model of the pancreas [3].

Additionally, according to Destexhe’s chemical synapse model, there are four type of

postsynaptic receptors: AMPA, NMDA, GABAA and GABAB. The postsynaptic

current measured from the whole cell recording of each receptor is shown in Fig.(5.1)

[4]. The AMPA and NMDA receptors will chemically bind with glutamatic acid,

110

5.1. Introduction 111

while γ-aminobutyric acid is the neurotransmitter that can be detected by the GABAA

and GABAB receptors. The profile of the neurotransmitter release from the chem-

ical synapse model of Dextexhe et al. [4] is assumed to be the brief pulse of 1mM

concentration in amplitude and 1ms in duration.Methods in Neuronal Modeling� Chapter � �

100 pA

10 ms

20 pA

200 ms

10 pA

10 ms

AMPA NMDAA B

C D

200 ms

10 pA

GABAA GABAB

Figure ��Best �ts of simpli�ed kinetic models to averaged postsynaptic currents obtained from whole�cell recordings�A� AMPA�kainate�mediated currents� B� NMDA�mediated currents� C� GABAA�mediated currents� D�GABAB�mediated currents� For all graphs� averaged whole�cell recordings of synaptic currents �noisytraces� identical description as in Fig� �� are represented with the best �t obtained using the simplestkinetic models �continuous traces�� Transmitter time course was a pulse of � mM and � ms duration in allcases �A� modi�ed from Destexhe et al�� c� C� modi�ed from Destexhe et al�� a� D� modi�ed fromDestexhe et al�� tting procedures described in Appendix B��

Figure 5.1: Postsynaptic current of (A) AMPA receptor, (B) NMDA receptor, (C)GABAA receptor and (D) GABAB receptor [4]

In this thesis, an implementation of the Destexhe’s chemical synapse model will be pre-

sented via a current-mode CMOS integrated circuit that operates in the weak inversion

region. By operating the CMOS integrated circuit in this region, we can both achieve a

low power consumption and the direct arithmetic computation. The main focus of this

work will be the electronic circuit realisation of the four postsynaptic receptor types

in the Destexhe’s chemical synapse: AMPA, NMDA, GABAA and GABAB. The

author sincerely believes that this work will ultimately pave the way for the creation of

5.2. Neural bridge 112

an artificial chemical synapse receptor which has the capability to sense actual neuro-

transmitter releases from the living neurons.

The sensor for the detection of glutamate (i.e. AMPA and NMDA receptors) is the

enzyme-immobilised ISFET with glutamate oxidase. The artificial glutamate stimulus

that represents a neurotransmitter release is established with a micro-tip glass elec-

trode, based on the iontophoresis technique, described in the previous chapter. Due to

the difficulty in accessing the GABA oxidase enzymes to make a γ-aminobutyric acid

ISFET, the GABAA and GABAB silicon synapse receptors will be verified electroni-

cally without a chemical interface.

This chapter will begin by giving two examples of a silicon neuron that has been used

to re-connect a broken neural signal path, which gave rise to the idea of a neural link or

a neural bridge. The following sections will then describe the implementation of each

of the chemical synapse receptors in the Destexhe model [4]. Towards the end of this

chapter, the CMOS implementation of the full postsynaptic circuit that combines the

Hodgkin-Huxley neuron circuit and the bio-inspired chemical synapse will be presented.

5.2 Neural bridge

Since the implementation of an electronic cochlear in the late 1980s [5], bio-inspired

circuits on the CMOS platform, have been employed in many applications. One of the

interesting applications is in neural prosthetic device or neural interfacing. The ultimate

aim of these devices is to replace the damaged or malfunction neurons. This device is

considered as a neural bridge which can be used to re-connect a break in the normal

neural signal path. Two examples of this neural bridge will be shown here.


5.2.1 Non-invasive neuron stimulus

Fig.(5.2) shows the diagram of the silicon synapse chip based on the experiment of Kaul

et al. [6]. The idea of this experiment is to connect an electronic circuit and live neurons

together using a non-invasive neuron stimulation. When neuron A (a presynaptic cell)

is stimulated with a signal via a capacitor (C), the excited neuron A will expel the

neurotransmitter agents that will be detected by the neuron B (a postsynaptic cell).

The membrane signal of the neuron B, according to this neurotransmitter change, will

be sensed via the gate of the transistor. The presynaptic neuron is the visceral dorsal 4

(VD4) and the postsynaptic neuron is the left pedal dorsal 1 (LPeD1). These neurons

were obtained from a pond snail.

C S DG

Oxide

Semiconductor

Electrolyte

Neuron A Neuron B

A B CSensory

input

Mortor

output

A B CSensory

input

Mortor

output

VLSI circuit

Dentate CA1

VLSI circuit

Dentate

CA1

CA3

Figure 5.2: A diagram based on Kaul’s experiment

5.2.2 Hippocampal neural bridge

Berger et al. [2] proposed the idea to use an integrated circuit to replace a damaged

neuron. This is an example of a neural bridge that bypasses and reroutes the neural

signal. The neuron substitution idea is illustrated in Fig.(5.3).


C S DG

Oxide

Semiconductor

Electrolyte

Neuron A Neuron B

Neuron

A

Sensory

input

Motor

output

Sensory

input

Motor

output

VLSI circuit

Dentate CA1

VLSI circuit

Dentate

CA1

CA3

Neuron

A

Neuron

B

Neuron

C

Neuron

C

Neuron

B

Figure 5.3: A circuit diagram for replacing a dysfunction central brain region with aVLSI system

In this implementation, the central nervous system (CNS) neurons in the hippocampus

region of the brain will be partially replaced by a silicon neuron. Fig.(5.4) shows the

slice view of the hippocampus which is comprised of dentate, CA1 and CA3 subregions.

The flow direction of the neural signal in this area starts from the dentate to CA3 and

CA1 respectively.

C S DG

Oxide

Semiconductor

Electrolyte

Neuron A Neuron B

A B CSensory

input

Mortor

output

A B CSensory

input

Mortor

output

VLSI circuit

Dentate CA1

VLSI circuit

Dentate

CA1

CA3

Figure 5.4: Diagram of the trisynaptic circuit of the hippocampus

In Berger’s work, the physiological properties of the CA3 neuron were modelled math-

5.3. Implementation of chemical synapse receptor 115

ematically and implemented with a VLSI circuit. This circuit was used in the place of

a normal CA3 neuron, as shown in Fig.(5.5).

C S DG

Oxide

Semiconductor

Electrolyte

Neuron A Neuron B

A B CSensory

input

Mortor

output

A B CSensory

input

Mortor

output

VLSI circuit

Dentate CA1

VLSI circuit

Dentate

CA1

CA3

Figure 5.5: Conceptual representation of replacing the CA3 with a VLSI model

The following section will present the implementation of a chemical synapse based on

the model of Destexhe et al. [4]. The AMPA and NMDA receptors will employ the

glutamate ISFET as the chemical front-end to sense the glutamate concentration change.

This glutamic stimulus, that represents the neurotransmitter signal, will be created via

the iontophoresis technique, which was described in Chapter 4.

5.3 Implementation of chemical synapse receptor

In this section, the kinetic model of the Destexhe’s chemical synapse, which was de-

scribed in section 2.4.3, will be implemented for all four synapse receptors (i.e. AMPA,

NMDA, GABAA and GABAB) using current-mode weak inversion CMOS circuits.

5.3.1 AMPA receptor

From the kinetic model for the post-synaptic transmission [4], the relevant equations

for the AMPA receptor are:


drAMPA

dt= αAMPA [T ](1− rAMPA)− βAMPArAMPA (5.1)

IAMPA = gAMPA

rAMPA(V − EAMPA) (5.2)

where αAMPA = 1.1× 106, βAMPA = 190, [T ] is the pulse shape of the neurotransmitter

signal with a time duration of 1ms and an amplitude of 1mM, while gAMPA

= 0.1nS.

The implementation of the circuit to mimic the rAMPA variable was accomplished by

using the Bernoulli cell. For the case of the AMPA receptor, the Bernoulli integrator

circuit shown in Fig.(5.6) has the following parameters that corresponds to eq.(5.3).

IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iout-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin2

Iin2+Id2

C2

I02

I02

IrGABAB

Id3

C3

I0u

I0u

Iout2

Iout3Iin3 Iin3Iin3Iin3

Iin3 Iin3 Iin3Id3 Id3 Id3

Iin4

Id4

Iout4

I04

I04

Figure 5.6: Bernoulli cell circuit used for implementing variable rAMPA

IdAMPA = CAMPAnUT (αAMPA[T ] + βAMPA)

IinAMPA = CAMPAnUT [T ]αAMPA

(5.3)

where n is the subthreshold parameter and UT is the thermal voltage. Let CAMPA =

1.5nF, so the pulse current (IinAMPA), a 1ms pulse, has a peak value at 55nA; and


the DC current, CAMPAnUTβAMPA = 9.5nA. The output current of this Bernoulli cell

(Iout−AMPA) is shown in eq.(5.4).

Iout−AMPA = rAMPAI0AMPA (5.4)

5.3.2 NMDA receptor

For the NMDA receptor, the first order kinetic model and the synaptic current are

given by:

drNMDA

dt= αNMDA [T ](1− rNMDA)− βNMDArNMDA (5.5)

INMDA = gNMDA

B(V )rNMDA(V − ENMDA) (5.6)

where αNMDA = 7.2× 104, βNMDA = 6.6, [T ] is the pulse shape of the neurotransmitter

signal with a time duration of 1ms and an amplitude of 1mM, while gNMDA

= 0.1nS.

For the case of the NMDA receptor, the Bernoulli integrator circuit shown in Fig.(5.7)

has the following parameters that corresponds to eq.(5.7).

IdNMDA = CNMDAnUT (αNMDA[T ] + βNMDA)

IinNMDA = CNMDAnUT [T ]αNMDA

(5.7)

Let CNMDA = 22nF, so the pulse current (IinNMDA), a 1ms pulse, has a peak value at

52.8nA; and the DC current, CNMDAnUTβNMDA = 4.84nA. The output current of this

Bernoulli cell (Iber−NMDA) is shown in eq.(5.8).


IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iber-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin2

Iin2+Id2

C2

I02

I02

IrGABAB

Id3

C3

I0u

I0u

Iout2



Iin4

Id4

Iout4

I04

I04

Figure 5.7: Bernoulli cell circuit used for implementing variable rNMDA

Iber−NMDA = rNMDAI0NMDA (5.8)

Recall eq.(5.6), the parameter B(V ) is required to calculate the current INMDA. The

parameter B(V ) was implemented by a sigmoid circuit in Fig.(5.8). The B(V ) param-

eter is shown in eq.(5.9).

B(V ) =1

1 + exp (−62V )[Mg2+]o3.57

(5.9)

where the intracellular of the magnesium concentration ([Mg2+]o) is 1mM. The output

current as the voltage function of this sigmoid circuit is given by:

IOut−Sig =ISig

1 + exp{ASig(V1−V2)(n2p+np)UT

}(5.10)

If the terminal V1 is grounded and the output current from the NMDA Bernoulli cell


IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iout-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin2

Iin2+Id2

C2

I02

I02

IrGABAB

Id3

C3

I0u

I0u

Iout2



Iin4

Id4

Iout4

I04

I04

Figure 5.8: Sigmoid circuit for B(V ) implementation

in Fig.(5.7) is used as the input current for the sigmoid cell (Isig), hence:

IOut−Sig =I0NMDArNMDA

1 + exp { ASig(−V2)(n2p+np)UT

}(5.11)

5.3.3 GABAA receptor

From the kinetic model for post-synaptic transmission [4], the relevant equations for the

GABAA receptor are:

drGABAAdt

= αGABAA [T ](1− rGABAA )− βGABAA rGABAA (5.12)

IGABAA = gGABAA

rGABAA (V − EGABAA) (5.13)

where αGABAA = 5.3 × 105, βGABAA = 180, [T ] is the pulse shape of neurotransmitter


signal with a time duration of 1ms and an amplitude of 1mM and gGABAA

= 0.1nS. The

implementation of the circuit to mimic the rGABAA variable was accomplished by the

Bernoulli cell. For the case of the GABAA receptor, the Bernoulli integrator circuit

shown in Fig.(5.9) has the following parameters that corresponds to eq.(5.14).

IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iout-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin2

Iin2+Id2

C2

I02

I02

IrGABAB

Id3

C3

I0u

I0u

Iout2



Iin4

Id4

Iout4

I04

I04

Figure 5.9: Bernoulli cell circuit used for implementing variable rGABAA

IdGABAA = CGABAAnUT (αGABAA [T ] + βGABAA)

IinGABAA = CGABAAnUT [T ]αGABAA

(5.14)

where n is the subthreshold parameter and UT is the thermal voltage. Let CGABAA =

820pF, so the pulse current (IinGABAA), a 1ms pulse, has a peak value at 136.6nA; and

the DC current, CGABAAnUTβGABAA = 4.92nA. The output current of this Bernoulli

cell (Iout−GABAA) is shown in eq.(5.15).

Iout−GABAA = rGABAAI0GABAA (5.15)


5.3.4 GABAB receptor

For the GABAB receptor, the first order kinetic models and the synaptic current are

given by:

drGABABdt

= K1 [T ](1− rGABAB )−K2rGABAB (5.16)

du

dt= K3rGABAB −K4u (5.17)

IGABAB = gGABAB

u4

u4 +Kd(V − EGABAB

) (5.18)

where K1 = 9 × 104M−1s−1, K2 = 1.2s−1, K3 = 180s−1, K4 = 34s−1, n = 4,

Kd = 100µM4, [T ] is the pulse shape of the neurotransmitter signal with a time duration

of 1ms and an amplitude of 1mM and gGABAB

= 0.1nS.

Implementation of the rGABAB and u variables required two Bernoulli cells in cascade as

shown in Fig.(5.10). The first Bernoulli cell creates the variable rGABAB . The relevant

design equations for this variable are:

Id1 = C1nUT (K1[T ] +K2)

Iin1 = C1nUT [T ]K1

IrGABAB = rGABAB I01

(5.19)

Let C1 = 22nF. The pulse current(Iin1) with 1ms pulse width has the maximum current

amplitude at 381nA and the DC current, C1nUTK2 = 1nA. The variable u was generated

by the second Bernoulli cell. The first order differential equation of the second Bernoulli

cell is given by eq.(5.20).


IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iber-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin1

Id1

C1

I01

I01

IrGABAB

Id2

C2

I0u

I0u

Iout-u



Iin4

Id4

Iout4

I04

I04

Figure 5.10: Bernoulli cell circuit used for implementing variables rGABAB and u

dIout−udt

+ (Id2

C2nUT) · Iout−u = (

1

C2nUT) · IrGABAB (5.20)

Substitute IrGABAB = rGABAB I01 from eq.(5.19) into eq.(5.20):

dIout−udt

+ (Id2

C2nUT) · Iout−u = (

I01

C2nUT) · rGABAB (5.21)

Rearrangement of eq.(5.17) yields:

du

dt+K4u = K3rGABAB (5.22)

By comparing eq.(5.21) and eq.(5.22), the parameters for the second Bernoulli cell are:


Id2 = C2nUTK4

I01 = C2nUTK3

Iout−u = I0uu

(5.23)

Let C2 = 10nF, so Id2 = 11nA and I01 = 33nA. To create the variable u4, the translinear

current multiplication circuit shown in Fig.(5.11) is required.

IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iout-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin2

Iin2+Id2

C2

I02

I02

IrGABAB

Id3

C3

I0u

I0u

Iout2



Iin4

Id4

Iout4

I04

I04

Figure 5.11: Translinear current multiplication circuit

The relationship between the input current (Iin3) and the output current (Iout3) is shown

in eq.(5.24).

Iout3 =(Iin3)4

(Id3)3(5.24)

Let Iin3 = Iout−u and Id3 = I0u, thus:

Iout3 = I0uu4 (5.25)

5.4. Implementation of the postsynaptic transmission 124

The term u4

u4+Kdcan be established by the translinear loop circuit shown in Fig.(5.12).

IinAMPA

IdAMPA

CAMPA

Iout-AMPA

I0AMPA

I0AMPA

IinNMDA

IdNMDA

CNMDA

Iout-NMDA

I0NMDA

I0NMDA

Iout-Sig

V1 V2

ISig

ASigASig

IinGABA

IdGABA

CGABA

Iout-GABA

I0GABA

I0GABA

A

A

A

A

A

A

Iin2

Iin2+Id2

C2

I02

I02

IrGABAB

Id3

C3

I0u

I0u

Iout2



Iin4

Id4

Iout4

I04

I04

Figure 5.12: Circuit implementation of function u4

u4+Kd

The relationship between the output current (Iout4) and the other three input currents

(Iin4, Id4 and I04) of the current mode divider circuit is shown in eq.(5.26)

Iout4 =Iin4I04

Id4(5.26)

Let Iin4 = I0uu4, Id4 = (I0uu

4 + I0uKd) and I04 = I0GABAB , we obtain:

Iout4 =u4

u4 +KdI0GABAB (5.27)

5.4 Implementation of the postsynaptic transmission

In this section, the postsynaptic potential of each chemical synapse receptors will be

presented with measured result. For the AMPA and NMDA receptors, the postsynap-


tic circuit of these two receptors employs the glutamate ISFET as the chemical input.

This input represents the change in the neurotransmitter concentration that is gener-

ated via the iontophoresis technique and is detected by the glutamate ISFET.

However, as stated earlier for the GABAA and GABAB receptors, an electrical signal

from an AC current source will be used to simulate the detected neurotransmitter signal.

This is due to the difficulty in accessing the enzyme GABA-oxidase for the modifica-

tion of ISFETs to detect γ-aminobutyric acid. This enzyme has not been extracted for

commercial use and there is only one publication that has reported on its extraction

process [7].

The postsynaptic potential of a chemical synapse is given by eq.(5.28) where Vm is the

postsynaptic potential, Cm is the equivalent membrane capacitance, INa is the current

from the sodium channel, IK is the current from the potassium channel IL is the current

from the leakage channel.

CmdVmdt

= −INa − IK − IL︸︷︷︸Hodgkin and Huxley model

−Isyn (5.28)

From the electronic circuit point of view, eq.(5.28) can be illustrated as shown in

Fig.(5.13). The shaded area represents the Hodgkin and Huxley neuron circuit. Imple-

mentation of INa and IK is based on the circuit realisation of Lazaridis et al. [8]. As

the conductance gain of sodium and potassium channels are considerably higher than

the synaptic or leakage conductance, the OTAs for Na and K current channels were im-

plemented from DeWeerth et al. shown in Fig.(3.9). Isyn is referred to IAMPA, INMDA,

IGABAA and IGABAB . The simulation results of the bionics chemical synapse receptors


were obtained from the Cadence on the AMS C35B3C3 CMOS process.

leakI

synI

+-

+-

+-

+-

mC

40 nI n

hmI mh3

0

leakE

synE

NaE

KE

mV

Figure 5.13: Circuit of the bionics postsynaptic chemical synapse

5.4.1 Postsynaptic circuit for the AMPA receptor

The equations related to the postsynaptic potential of the AMPA receptor are shown

in eq.(5.29).

CmdVmAMPA

dt= −INa − IK − IL − IAMPA

IAMPA = gAMPArAMPA(Vm − EAMPA)(5.29)

IAMPA in eq.(5.29) was implemented using the Bernoulli cell in Fig.(5.6) and the low

transconductance gain OTA shown in Fig.(5.14). The relationship between the output

current (Iout), the input differential voltage (Vin+ − Vin−) and the input bias current


(Ix1) is shown in eq.(5.30). The circuit analysis of this OTA can be viewed in section

3.5.

Iout =(1− 1

np)Ix1(Vin+ − Vin−)

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT(5.30)

where np is the subthreshold slope parameter of PMOS, nn is the subthreshold slope

parameter of NMOS and UT is the thermal voltage.

SigoutI

SigI

1V2VSigASigA

2inI 2outI

02I

2dI

03I

3dI

02I

03I2c3c

applyI

r

+

----

-

++++

Electrode 1

Electrode 2

Micropipette

inVinV

outI

1xI

2xI

6.5

6.5

35.0

100

6.5

2.67

35.0

200

6.5

6.5

6.5

6.5

6.5

6.5

6.5

2.67

35.0

100

35.0

100

35.0

100

35.0

100

35.0

100

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

35.0

200

6.5

6.5

6.5

6.5

inVinV

outI

1xI

2xI

PBS pH 7

bV

outrI

bI

bI

4

40

4

100

4

40

4

40

4

40

leakI

synI

+

-

+

-

+

-

+

-

mC

4

0 nI n

hmI mh

3

0

leakE

synE

NaE

KE

mV

SigA

1inI 1outI01I

01I11 din II

1C

2inI

02I

02I

3dI

2outIBrGABAI

uI0

uI0

22 din II

2C 3C

3inI3outI3inI

3inI03I

3inI

3inI

03I

3inI

3inI

03I

4inI4outI

04I

4dI

04I

Figure 5.14: Low transconductance gain OTA circuit

The output current (Iout−AMPA) of the Bernoulli cell shown in Fig.(5.6) was designated

as the input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit configuration,


the output current of the OTA is:

Iout =(1− 1

np)I0AMPA

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT· rAMPA(Vin+ − Vin−) (5.31)

Iout in eq.(5.31) and IAMPA in eq.(5.29) are comparable and it can be concluded that:

gAMPA =(1− 1

np)I0AMPA

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT(5.32)

From the biological model of the AMPA receptor, the conductance gain of AMPA

(gAMPA) is 0.1nS [4]. Thus, I0AMPA can be calculated based on this expression.

I0AMPA =gAMPAnn(1 + nn

np+ n2

n)(n2n + nn + 1)16UT

(1− 1np

)(5.33)

With nn = 1.3, np = 1.28 and UT=25.82mV @ 300K, I0AMPA is 3.6nA. The amplitude

of IdAMPA based on CAMPA = 1.5nF is 9.5nA.

The glutamate ISFET or the neurotransmitter sensor of this receptor couples the glu-

tamate concentration change via the current-mode ISFET readout circuit of Shep-

herd et al. [9]. The output current of this readout circuit represents IinAMPA or

CAMPAnUT [T ]αAMPA in eq.(5.14).

CAMPAnUT [T ]αAMPA = Ibe2γnUT e

−2VbAMPAnUT [ions] (5.34)

Rearranging eq.(5.34) yields:


VbAMPA =−nUT

2ln

(nCAMPAUTαAMPA

Ibe2γnUT

)(5.35)

In eq.(5.34), both [T ] and [ions] represent the glutamate ions concentration at 1 mM.

Perturbation of the glutamate ions was carried out by using a micropipette filled with

1 M glutamate solution with all the parameters as described in section 4.5 of Chapter

4. The current source used for the glutamate injection was a Keithley 6221 AC current

source. In this case, the current amplitude was -0.4µA (see more detail in chapter 4,

section 4.5) and VbAMPA is 284.25mV. The overall circuit for the AMPA receptor is

shown in Fig.(5.16). The measured and the simulation results are shown in Fig.(5.15).

When the current amplitude was set to a positive value, no response was observed.

��

��

��

��

��

��

��

��

��

��

Figure 5.15: Measured vs. simulation results for the AMPA receptor

5.4.2 Postsynaptic circuit for the NMDA receptor

The equations related to the postsynaptic potential of the NMDA receptor are shown

in eq.(5.36).


leak

I

+ - + -

+ - + -

mC

4

0n

In

hm

Im

h

3

0

leak

EN

aE

KE

AM

PA

E

440

4

100

440

440

440

50

nA

50

nA

310

310

310

310

310

35

35

35

310

310

bA

MP

AV

AM

PA

dI1

AM

PA

C1

AM

PA

I 01

mA

MP

AV

310

310

310

35

310

310

leak

I

+ - + -

+ - + -

mC

4

0n

In

hm

Im

h

3

0

leak

EN

aE

KE

440

4

100

440

440

440

50

nA

50

nA

NM

DA

E

310

310

mN

MD

AV

Sig

A

mN

MD

AV

310

310

310

310

310

310

310

310

310

310

310

310

35

35

35

35310

310

310

310

310

310

bN

MD

AV

NM

DA

dI1

NM

DA

C1

NM

DA

I 01

leak

I

+ - + -

+ - + -

mC

4

0n

In

hm

Im

h

3

0

leak

EN

aE

KE

310

310

310

310

310

35

35

35

310

310

AG

AB

AdI

1

AG

AB

AC

1A

GA

BA

I 01

310

310

310

35

310

310

AG

AB

AE

Am

GA

BA

V

Ain

GA

BA

I

Cu

rre

nt

mo

de

ISF

ET

re

ad

ou

t

Lo

g d

om

ain

filt

er

for

AM

PA

Tra

ns

co

nd

uc

tan

ce

am

plifi

er

Cu

rre

nt

mo

de

ISF

ET

re

ad

ou

tL

og

do

ma

in

filt

er

for

NM

DA

Tra

ns

co

nd

uc

tan

ce

am

plifi

er

Lo

g d

om

ain

filt

er

for

GA

BA

A

Tra

ns

co

nd

uc

tan

ce

am

plifi

er

02

I

2C

3dI

2out

IB

rGA

BA

I

uI 0

3C

03

I

04

I

leak

I

+ - + -

+ - + -

mC

4

0n

In

hm

Im

h

3

0

leak

EN

aE

KE

BG

AB

AE

Bm

GA

BA

V

310

310

310

310

310

310

310

310

310

2dI

35

35

35

35

35

310

310

310

35

310

310

35

35

310

310

310

310

310

310

310

310

310

310

310

310

35

35

35

35

35

35

35

35

310

310

310

310

310

310

310

310

35

35

35

35

310

310

310

310

310

310

310

310

310

310

310

udI

K0

Bin

GA

BA

I

Sig

mo

id c

irc

uit

fo

r

B(V

)

Tra

ns

co

nd

uc

tan

ce

am

plifi

er

Cu

rre

nt

mo

de

div

ide

r c

irc

uit

Cu

rre

nt

mo

de

4th

po

we

r c

irc

uit

Lo

g d

om

ain

filt

er

for

GA

BA

B

Fig

ure

5.16

:F

ull

sch

emat

icof

aB

ion

ics

chem

ical

syn

apse

for

the

AM

PA

rece

pto

r


CmdVmNMDA

dt = −INa − IK − IL − INMDA

INMDA = gNMDA

B(V )rNMDA(V − ENMDA)(5.36)

INMDA in eq.(5.36) was implemented using the Bernoulli cell in Fig.(5.7), the sigmoid

circuit in Fig.(5.8) and the low transconductance gain OTA in Fig.(5.14). The output

current (Iout−Sig) of the combined circuits, shown in eq.(5.11), was designated as the

input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit configuration, the

output current of the OTA is given by:

Iout =(1− 1

np)I0NMDA

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT· rNMDA


}(Vin+ − Vin−) (5.37)

Iout in eq.(5.37) and INMDA in eq.(5.36) are comparable and it can be concluded that:

gNMDA =(1− 1

np)I0NMDA

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT(5.38)

1

1 + exp (−62V )[Mg2+]o3.57mM

=1


}(5.39)

From the biological model of the NMDA receptor, the conductance gain of NMDA

(gNMDA) is 0.1nS [4]. Thus, I0NMDA is 3.6nA with nn = 1.3, np = 1.28 and UT =

25.82mV @ 300K. The value of IdNMDA based on CNMDA = 22nF is 4.84nA. ASig is

1.3 based on the assumption that the magnesium concentration is 1mM. The glutamate

ISFET or the neurotransmitter sensor of this receptor couples the glutamate concen-

tration change via the current-mode ISFET readout circuit of Shepherd et al. [9]. The

output current of this readout circuit represents IinNMDA or CNMDAnUT [T ]αNMDA in

eq.(5.9).


CNMDAnUT [T ]αNMDA = Ibe2γnUT e

−2VbNMDAnUT [ions] (5.40)

Rearranging eq.(5.40) yields:

VbNMDA =−nUT

2ln

(nCNMDAUTαNMDA

Ibe2γnUT

)(5.41)

In eq.(5.40), [T ] and [ions] both represent the glutamate ions concentration at 1 mM.

Perturbation of the glutamate ions was carried out by using a micropipette filled with

1 M glutamate solution with all the parameters as described in section 4.5 of Chapter

4. The current source used for the glutamate injection was a Keithley 6221 AC current

source. In this case, the current amplitude was -0.4µA and VbNMDA was 284.92mV.

The overall circuit for the NMDA receptor is shown in Fig.(5.18). The measured and

circuit simulation results are shown in Fig.(5.17). When the current amplitude was set

to a positive value, no response was observed.

��

��

��

��

��

��

��

��

Figure 5.17: Measured vs. simulation results for the NMDA receptor

5.4. Implementation of the postsynaptic transmission 133le

ak

I

+ - + -

+ - + -

mC

4

0n

In

hm

Im

h

3

0

leak

EN

aE

KE

AM

PA

E

440

4

100

440

440

440

50

nA

50

nA

310

310

310

310

310

35

35

35

310

310

bA

MP

AV

AM

PA

dI1

AM

PA

C1

AM

PA

I 01

mA

MP

AV

310

310

310

35

310

310

leak

I

+ - + -

+ - + -

mC

4

0n

In

hm

Im

h

3

0

leak

EN

aE

KE

440

4

100

440

440

440

50

nA

50

nA

NM

DA

E

310

310

mN

MD

AV

Sig

A

mN

MD

AV

310

310

310

310

310

310

310

310

310

310

310

310

35

35

35

35310

310

310

310

310

310

bN

MD

AV

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DA

dI1

NM

DA

C1

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DA

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+ - + -

mC

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In

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3

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310

310

310

310

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35

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AG

AB

AC

1A

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BA

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310

310

310

35

310

310

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AB

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GA

BA

V

Ain

GA

BA

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er

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tan

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er

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nt

mo

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ET

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ad

ou

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og

do

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in

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er

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for

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uc

tan

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02

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04

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+ - + -

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4

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In

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Im

h

3

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AB

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GA

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310

310

310

310

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310

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irc

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fo

r

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ide

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Cu

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de

4th

po

we

r c

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uit

Lo

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ain

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for

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Fig

ure

5.1

8:

Fu

llsc

hem

atic

ofa

Bio

nic

sch

emic

alsy

nap

sefo

rth

eN

MD

Are

cep

tor


5.4.3 Postsynaptic circuit for the GABAA receptor

The equations related to the postsynaptic potential of the GABAA receptor are shown

in eq.(5.42).

CmdVmGABAA

dt= −INa − IK − IL − IGABAA

IGABAA = gGABAA rGABAA (Vm − EGABAA)

(5.42)

IGABAA in eq.(5.42) was implemented using the Bernoulli cell in Fig.(5.8) and the low

transconductance gain OTA in Fig.(5.14). The relationship between the output current

(Iout), the input differential voltage (Vin+ − Vin−) and the input bias current (Ix1) is

shown in eq.(5.30).

The output current (Iout−GABAA) of the Bernoulli cell shown in Fig.(5.8) was designated

as the input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit configuration,

the output current of the OTA is:

Iout =(1− 1

np)I0GABAA

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT· rGABAA(Vin+ − Vin−) (5.43)

Iout in eq.(5.43) and IGABAA in eq.(5.42) are comparable and it can be concluded that:

gGABAA =(1− 1

np)I0GABAA

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT(5.44)

From the biological model of the GABAA receptor, the conductance gain of GABAA

(gGABAA ) is 0.1nS [4]. Thus, I0GABAA can be calculated based on this expression:


I0GABAA =gGABAAnn(1 + nn

np+ n2

n)(n2n + nn + 1)16UT

(1− 1np

)(5.45)

With nn = 1.3, np = 1.28 and UT = 25.82mV @ 300K, I01GABAA is 3.6nA. The value of

Id1GABAA based on C1GABAA = 820pF is 4.92nA, while the input pulse current IinGABAA

has the maximum peak at 136.6nA. Fig.(5.20) shows the overall circuit of the GABAA

receptor. The measured and simulation results are shown in Fig.(5.19).

��

��

��

��

��

��

��

��

Figure 5.19: Measured vs. simulation results for the GABAA receptor


leakI

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+

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+

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3

0

leakENaE

KEAMPAE

4

40

4

100

4

40

4

40

4

40

50 nA

50 nA

3

10

3

10

3

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10

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10

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5

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5

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5

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AMPAC1 AMPAI01

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3

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10

3

5

3

10

3

10

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+

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+

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+

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3

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4

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4

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4

40

4

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10

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NMDAC1

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3

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3

10

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5

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3

10

3

10

3

10

3

5

3

10

3

10

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AinGABAI

Current mode

ISFET readout

Log domain

filter for AMPATransconductance

amplifier

Current mode

ISFET readoutLog domain

filter for NMDA

Transconductance

amplifier

Log domain

filter for GABAA

Transconductance

amplifier

02I

2C3dI

2outIBrGABAI

uI0

3C

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+

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+

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+

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10

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103

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3

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3

10

udIK 0

BinGABAI

Sigmoid circuit for

B(V)

Transconductance

amplifier

Current mode

divider circuit

Current

mode 4th

power circuitLog domain

filter for GABAB

Figure 5.20: Full schematic of a Bionics chemical synapse for the GABAA receptor

5.4.4 Postsynaptic circuit for the GABAB receptor

The equations related to the postsynaptic potential of the GABAA receptor are shown

in eq.(5.46).

CmdVmGABAB

dt= −INa − IK − IL − IGABAB

IGABAB = gGABAB

u4

u4 +Kd(V − EGABAB

)(5.46)

IGABAB in eq.(5.46) was implemented using the Bernoulli cell in Fig.(5.10), the current

multiplication circuit in Fig.(5.11), the current divider circuit in Fig.(5.12) and the low

transconductance gain OTA in Fig.(5.14). The relationship between the output current

(Iout), the input differential voltage (Vin+ − Vin−) and the input bias current (Ix1) is

shown in eq.(5.30).


The output current (Iout4) of the circuit that implements u4

u4+Kdshown in Fig.(5.12)

was designated as the input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit

configuration, the output current of the OTA is given by:

Iout =(1− 1

np)I0GABAB

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT· u4

u4 +Kd(Vin+ − Vin−) (5.47)

Iout in eq.(5.47) and IGABAB in eq.(5.46) was comparable and it can be concluded that:

gGABAB =(1− 1

np)I0GABAB

nn(1 + nnnp

+ n2n)(n2

n + nn + 1)16UT(5.48)

From the biological model of the GABAB receptor, the conductance gain of GABAB

(gGABAB ) is 0.1nS [4]. Thus, I0GABAB can be calculated based on this expression:

I0GABAB =gGABABnn(1 + nn

np+ n2

n)(n2n + nn + 1)16UT

(1− 1np

)(5.49)

With nn = 1.3, np = 1.28 and UT = 25.82mV @ 300K, I0GABAB is 3.6nA. Based on C1

= 22nF, C2 = 10nF, Id1 = 1nA, Id2 = 11nA, I01 = 33nA, I0u = 10nA and I0uKd = 1nA,

while the input pulse current IinGABAB has the maximum peak at 381nA. Fig.(5.22)

shows the overall circuit of the GABAB receptor. The measured and simulation results

are shown in Fig.(5.21).

A microphotograph of the fabricated chemical synapse integrated circuit is shown in

Fig.(5.23). The chip area of the four chemical synapses is 1120 × 1120 µm2. The total

power dissipation of all the circuits in this chip is 168.3µW from a 3.3V supply. The

printed circuit board used in this thesis was designed in Orcad version 15.1. Dimension

of this board is 350mm x 350mm. A photograph of this PCB is shown in Fig.(5.24).


��

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Figure 5.21: Measured vs. simulation results for the GABAB receptor

The overall experimental setup is shown in Fig.(5.25). The closed up illustration of the

tip of the micropipette and the glutamate ISFET is shown in Fig.(5.26).

Figure 5.23: Microphotograph of the fabricated chemical synapse


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35

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310

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MP

AV

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310

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35

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310

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310

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35

35

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ure

5.2

2:

Fu

llsc

hem

atic

ofa

Bio

nic

sch

emic

alsy

nap

sefo

rth

eGABAB

rece

pto

r


Figure 5.24: The photograph of bionics chemical synapse chip test and application board

Figure 5.25: Experimental setup for bionics chemical synapse chip


Figure 5.26: Closed up picture of the glutamate ISFET and the tip of the micropipette

The parameters for the implementation of each receptor are summarised in Table (5.1).

Table 5.1: AMPA, NMDA, GABAA and GABAB parametershhhhhhhhhhhhhhhParameter

Receptor(x)AMPA NMDA GABAA GABAB

Ex(mV) 1070 1070 970 970ENa(mV) 1115 1115 1115 1115EK(mV) 988 988 988 988Eleak(mV) 989.3 989.3 989.3 989.3Ileak(nA) 34 34 34 34Cm(pF) 3.14 3.14 3.14 3.14

The measured results of the postsynaptic circuit in AMPA, NMDA, GABAA and

GABAB in Fig.(5.15), (5.17), (5.19) and (5.21) respectively, have a noisy reading. As

the output of the OTA has a high output impedance, the thermal noise at this node

was high and it also had a tendency to pick up the 50Hz line signal. One possible

solution to reduce this noise is to introduce of a metallic case to shield the test board.

5.5. Summary 142

The experiment from Jakobson et al. [10] concluded that the intrinsic MOSFET noise

dominates the noise characteristic of ISFETs. The drain current noise spectra of ISFETs

(SID) operated in weak inversion region [10] is shown in eq.(5.50)

SID =C2inv

(Cox + CD)4· q4Not

(kT )2WLI2D

1

f(5.50)

where Cinv, Cox and CD are the inversion, oxide and depletion capacitance per area,

Not is the effective oxide traps density per unit area, k is the Boltzmann’s constant, T

is the absolute temperature, W and L are the width and length of the MOSFET, ID is

the DC drain current and f is the frequency bandwidth. From eq.(5.14), noise on the

ISFETs can be minimised if the gate area (WL) is maximised.

Also, the measured results did not match perfectly with the simulation results. One of

possible reasons is that there is a temperature difference between the simulation and test

bench environment. This is because weak inversion circuits are temperature dependent

(i.e. thermal voltage term, UT ). From Fig.(5.15) and (5.19), it can be observed that

there is difference time delay between simulation and measured results. This delay is

due to a parasitic body source capacitance of the input MOSFETs.

5.5 Summary

In this chapter, the first bio-inspired chemical synapse with glutamate ISFETs as the

chemical front-end on silicon integrated circuit has been presented. Based on the chem-

ical synapse model of Destexhe, the AMPA and NMDA receptors were fully imple-

mented with glutamate ISFETs in analogue current-mode subthreshold CMOS. The

measured results of the electro-physiological characteristics of these receptors match

5.5. Summary 143

well with their models in circuit simulation. With this bio-inspired chemical synapse

integrated circuit, a complete CMOS chemical synapse for the receptors GABAA and

GABAB will be readily achieved with the introduction of a γ-aminobutyric acid (GABA)

sensor [7] for the GABAA and GABAB receptors.

The chemical synapse implementation accomplished in this work has the potential to

create the artificial receptors of the chemical synapse. These synthetic receptors can be

used as a neural link or neural bridge to bypass damaged or terminated neural signal

path. This will be possible, in the future, when the ISFET and the processing circuit are

integrated onto the same chip. Another challenge is to match the ISFET’s sensing area

to the synapse of a pre-synaptic neuron to detect the actual neurotransmitter emitted.

References

[1] C. Toumazou, J. Georgiou, and E. M. Drakakis, “Current-mode analogue cir-

cuit representation of hodgkin and huxley neuron equations,” Electronics Letters,

vol. 34, no. 14, pp. 1376–1377, 1998.

[2] T. W. Berger, A. Ahuja, S. H. Courellis, S. A. Deadwyler, G. Erinjippurath, G. A.

Gerhardt, G. Gholmieh, J. J. Granacki, R. Hampson, M. C. Hsaio, J. Lacoss, V. Z.

Marmarelis, P. Nasiatka, V. Srinivasan, D. Song, A. R. Tanguay, and J. Wills,

“Restoring lost cognitive function,” Engineering in Medicine and Biology Magazine,

IEEE, vol. 24, no. 5, pp. 30–44, 2005.

[3] P. Georgiou and C. Toumazou, “A silicon pancreatic beta cell for diabetes,”

Biomedical Circuits and Systems, IEEE Transactions on, vol. 1, no. 1, pp. 39–

49, 2007.



MIT Press, 1998, pp. 1–26.

[5] R. F. Lyon and C. Mead, “An analog electronic cochlea,” Acoustics, Speech and

Signal Processing, IEEE Transactions on, vol. 36, no. 7, pp. 1119–1134, 1988.

144

REFERENCES 145

[6] R. A. Kaul, N. I. Syed, and P. Fromherz, “Neuron-semiconductor chip with chemical

synapse between identified neurons,” Phys. Rev. Lett., vol. 92, p. 038102, Jan

2004. [Online]. Available: http://link.aps.org/doi/10.1103/PhysRevLett.92.038102

[7] A. Yamamura, Y. Kimura, S. Tamai, and K. Matsumoto, “Gamma-aminobutyric

acid (gaba) sensor using gaba oxidase from penicillium sp. kait-m-117,” ECS Meet-

ing Abstracts, vol. 802, no. 46, pp. 2832–2832, 08/29 2008.

[8] E. Lazaridis and E. M. Drakakis, “Full analogue electronic realisation of the

hodgkin-huxley neuronal dynamics in weak-inversion cmos,” in Engineering in

Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Con-

ference of the IEEE, 2007, pp. 1200–1203.


inversion isfets,” Circuits and Systems I: Regular Papers, IEEE Transactions on,

vol. 52, no. 12, pp. 2614–2619, 2005.

[10] C. G. Jakobson and Y. Nemirovsky, “1/f noise in ion sensitive field effect transistors

from subthreshold to saturation,” Electron Devices, IEEE Transactions on, vol. 46,

no. 1, pp. 259–261, 1999.

Chapter 6

Conclusion and Future Work

A silicon chemical synapse implemented using subthreshold CMOS circuits and enzyme-

modified ISFETs as neurotransmitter sensors was implemented in this thesis. This

implementation is the very first artificial synapse with the ability to sense a neurotrans-

mitter (glutamate). The significance of this work is that it can be further developed

into a new prosthetic tool to reconnect breaks in the neural pathway, due to damaged or

deteriorated nervous cells. To create this artificial synapse, a sub-nano-Siemens opera-

tional transconductance amplifier with a bulk driven input and the double differential

pairs techniques was introduced. Typical ISFETs were modified with glutamate oxi-

dase (GluOX) and merged with current-mode ISFET readout circuits to produce linear

glutamate concentration sensors. Furthermore, the mathematical models for Destexhe’s

chemical synapse was realised and formulated in weakly inverted CMOS circuits.

146

6.1. Contribution 147

6.1 Contribution

The concept of applying electronic circuits to bio-inspired systems was introduced in

Chapter 2. Initially, the principles of the neuron communication system were presented,

such as the physical characteristics of neurons, the presence and function of the ion chan-

nels, the generation of the action potential and the different mathematical models of

the action or membrane potential. Additionally, the fundamentals of synapses and the

chemical synapse mathematical model were described. Furthermore, different types of

neuro-inspired circuits, both synapse and neuron, were also reviewed.

Chapter 3 began by laying out the specification of the operational transconductance

amplifier (OTA) that is required for this application, a transconductance gain in the

sub-nano Siemens range with a nano-Ampere range bias current. According to the Des-

texhe’s chemical synapse model, the minimum conductance of each receptor is 0.1nS.

This requirement was fulfilled by a novel OTA that combines several OTA design tech-

niques, which are: the bulk driven MOSFET and the drain current normalisation.

Circuit analysis of the OTA topologies was described in detail, from the elementary

differential pair to the novel technique that combined the bulk driven with drain cur-

rent normalisation. Circuit simulation confirmed that this new OTA design was able to

acquire a transconductance gain of 0.1nS with a 3.6nA bias current.

In this work, the ISFET was used as the coupler between the electronics and the chem-

ical world. The enzyme immobilised ISFET functioned as the neurotransmitter sensor

for the bionics chemical synapse. Also, the principle of the ISFET and the ion per-

turbation technique called iontophoresis were introduced and explained in Chapter 4.

Firstly, the physical details and the chemical sensitivity of the ISFET were outlined.

6.1. Contribution 148

Secondly, the procedure carried out to immobilise the ISFET with the glutamate oxi-

dase enzyme was given. Furthermore, a current-mode ISFET readout circuit (H-cell)

in [1] was adopted to achieve a linear relationship between the ion concentration and

output, compared to the non-linear voltage-mode readout circuit in [2]. This gluta-

mate ISFET and H-cell were combined to create the first linear and sensitivity-tunable

glutamate sensor. Lastly, the iontophoresis technique used for generating the fast ion

flow was shown and the validity of this method was confirmed via an experiment. This

experiment was considered as the first iontophoresis technique to generate and verify a

millisecond H+ perturbation on the ISFET.

The integration of the glutamate ISFET with the current-mode CMOS circuits formed

the bionics chemical synapse as shown in Chapter 5. The log domain filter, the sigmoid

differential pair, the sub-nano Siemens OTA and the translinear circuit, all operated

in the weak inversion region were designed to perform the mathematical model of the

Destexhe’s chemical synapse. The iontophoresis technique was employed as the vir-

tual glutamate neurotransmitter release. Full implementation of the chemical synapse

receptors was carried out for the AMPA and NMDA receptors. These artificial chem-

ical synapses can be considered as a novel bionics chemical synapse implementation

which has an actual chemical input. The measured results from a fabricated chip and

the simulation results of the artificial chemical synapse exhibit good matching in the

post-synaptic response.

All publications related to this thesis can be found in Appendix A at the end of this

thesis.

6.2. Recommendation for Future Work 149

6.2 Recommendation for Future Work

Future developments according to the contents in this thesis are proposed in the follow-

ing areas:

6.2.1 Integration of the components on the same chip

For practical use in the future, this CMOS chemical synapse should be amended to have

all the discrete component, such as the capacitors and the ISFETs, integrated onto the

same chip.

The capacitor value being used currently in the Bernoulli cell of each receptor is in the

order of nano Farads. This range of capacitance occupies an area of about one millimetre

square on silicon. As the capacitance is linearly proportional to the chip area, reduc-

tions in the magnitude of the bias currents, for instance: IdAMPA in eq.(5.3), IdNMDA

in eq.(5.7), IdGABAA in eq.(5.14), Id1 in eq.(5.19) and Id2 in eq.(5.23)) are examples of

ways to economise the chip area. Another possibility is to employ circuit techniques

such as the active capacitor multiplier [3], to enlarge the small-on-chip capacitance.

The ISFETs that function as the neurotransmitter sensors of the CMOS chemical

synapse circuit should also be integrated onto the same chip as the processing cir-

cuit. The unmodified CMOS ISFET has been pioneered since 2000 [4]. The same chip

integration of the chemical sensors and the electronic circuits will lead to a potentially

implantable or in-vivo nerve bridge in the future.


6.2.2 The non-invasive and direct extracellular glutamate detector

Measurements of extracellular neurotransmitter is vital for neurologists to understand

more about neuron physiology and behaviour. Glutamate is one of the neurotransmitter

that have been studied via extracellular measurements because of its role in some func-

tions of the brain [5] and in Alzheimer’s disease [6]. Two methods have been pioneered

for this measurement: the microdialysis technique [7] and the visual optical method [8].

For extracellular glutamate measurement under the microdialysis technique, a penetra-

tion of the neuron is required. Also, this technique has limitations in rapid and local

concentration detection [8]. The optical technique on extracellular can measure local

concentration for each individual cell of neurons. However, it is an indirect measure-

ment of glutamate concentration and requires an optical tool to interpret the final result.

The linear current-mode readout circuit and the glutamate ISFET can be combined

and used as an electronic extracellular glutamate sensor. The ability of ISFETs as a

real time and fast chemical sensor has been proven [9]. This tool can be considered as a

non-invasive and real-time extracellular glutamate detector which could be potentially

used to record glutamate activity on synapses to understand complex brain processes,

or even learning and memory mechanisms.

6.2.3 Live neuron experiment

As the ultimate objective of this work is to pave the way for the development of a medical

treatment that will be able to re-connect broken neural signal path from damaged nerve

cells, therefore an experiment on this bionics chemical synapse with a live neurons should


be carried out as the first step towards this goal. This requires the cooperation of the

biologists who are capable of performing experiments with cell cultures. An example

of an experiment on the chemical synapse in cell cultures was demonstrated in Kaul’s

PhD work [10]. In his work, two types of synapse cells, exhibitatory and inhibitatory,

were extracted from a snail (Lymnaea stagnalis). These extracted neuron cells can be

used as the live neuron interface with the bionics chemical synapse.

References



on, vol. 52, no. 12, pp. 2614–2619, 2005.



pp. 1499–1508, 1980.

[3] G. A. Rincon-Mora, “Active capacitor multiplier in miller-compensated circuits,”

Solid-State Circuits, IEEE Journal of, vol. 35, no. 1, pp. 26–32, 2000.

[4] B. Palan, K. Roubik, M. Husak, and B. Courtois, “CMOS ISFET-based structures

for biomedical applications,” in Microtechnologies in Medicine and Biology, 1st

Annual International, Conference On. 2000, 2000, pp. 502–506.

[5] W. McEntee and T. Crook, “Glutamate: its role in learning, memory,

and the aging brain,” Psychopharmacology, vol. 111, pp. 391–401, 1993,

10.1007/BF02253527. [Online]. Available: http://dx.doi.org/10.1007/BF02253527

[6] M. R. Hynd, H. L. Scott, and P. R. Dodd, “Glutamate-mediated excitotoxicity and

neurodegeneration in alzheimers disease,” Neurochemistry international, vol. 45,

no. 5, pp. 583–595, 10 2004.

152

REFERENCES 153

[7] s. Fallgren and R. Paulsen, “A microdialysis study in rat brain of dihydrokainate,

a glutamate uptake inhibitor,” Neurochemical Research, vol. 21, pp. 19–25, 1996,

10.1007/BF02527667. [Online]. Available: http://dx.doi.org/10.1007/BF02527667

[8] S. Okumoto, L. L. Looger, K. D. Micheva, R. J. Reimer, S. J. Smith, and W. B.

Frommer, “Detection of glutamate release from neurons by genetically encoded

surface-displayed fret nanosensors,” Proceedings of the National Academy of Sci-

ences of the United States of America, vol. 102, no. 24, pp. 8740–8745, June 14

2005.

[9] S. Thanapitak, P. Pookaiyaudom, P. Seelanan, F. J. Lidgey, K. Hayatleh, and

C. Toumazou, “Verification of ISFET response time for millisecond range ion stim-

ulus using electronic technique,” Electronics Letters, vol. 47, no. 10, pp. 586–588,

2011.

[10] R. Kaul, “Chemical synapses on semiconductor chips,” Ph.D. dissertation, Tech-

nischen Universitat Munchen, 2007.

Appendix A

Publications

Journal Papers

� S. Thanapitak and C. Toumazou, “Bionic chemical synapse,” under revision for

Biomedical Circuits and Systems, IEEE Transactions on

Electronics Letters

� S. Thanapitak, P. Pookaiyaudom, P. Seelanan, F. J. Lidgey, K. Hayatleh, and

C. Toumazou, “Verification of isfet response time for millisecond range ion stim-

ulus using electronic technique,” Electronics Letters, vol. 47, no. 10, pp. 586–588,

2011.

Conference Papers

� S. Thanapitak and C. Toumazou, “Towards a bionic chemical synapse,” in Circuits

and Systems, 2009. ISCAS 2009. IEEE International Symposium on, 2009, pp.

677–680.

154

Appendix B

PCB outline of Bionics Chemical

Synapse

155

1565 5

4 4

3 3

2 2

1 1

DD

CC

BB

AA

GN

D

3.3V_CHIP

OUT_AMPA_PRE

OUT_NMDA_PRE

OU

T_G

AB

AA

_P

RE

OUT_GABAB_PRE

ELEAK

ECH

VB

GND

GN

D

I_10nA

I_B

ET

A

ST

IM_A

MP

A

STIM_NMDA

ST

IM_G

AB

AA

ST

IM_G

AB

AB

ILE

AK I0

GN

D

GN

D

D100

D200

D400

D800

IB2_1

IB2_1

OU

T_T

_1

OU

T_T

_2

BIA

S_T

_1

BIA

S_T

_2

IN_T

_1

IN_T

_2

U101

5_C

ON

U101

5_C

ON

11

22

33

44

55

U41

C_22nF

U41

C_22nF

+1

-2

U1

ISF

ET

_44_C

HIP

U1

ISF

ET

_44_C

HIP

C_AMPA7

OUT_AMPA8

V_GB9

V_GA10

SOURCE11

D212

D113

OUT_NMDA14

C_NMDA15

STIM_NMDA16

NC217

BIA

S_T

_2

18

OU

T_T

_2

19

IT_10nA

_2

20

IN_T

_2

21

I_10nA

22

I_B

ET

A23

OU

T_G

AB

AA

24

C_G

AB

AA

25

ST

IM_G

AB

AA

26

GN

D27

NC

328

VB29

S_COMMON30

ELEAK31

ECH32

D80033

D40034

D20035

D10036

VDD37

OUT_GABAB38

NC439

ST

IM_G

AB

AB

40

C2_G

AB

AB

41

C1_G

AB

AB

42

ILE

AK

43

I044

IN_T

_1

1

IT_10nA

_1

2

OU

T_T

_1

3

BIA

S_T

_1

4

ST

IM_A

MP

A5

NC

16

U38

C_22nF

U38

C_22nF

+1

-2

U39

C_820pF

U39

C_820pF

+1

-2

U40

C_1.5

nF

U40

C_1.5

nF

+1

-2

U42

C_10nF

U42

C_10nF

+1

-2

Fig

ure

B.1

:P

CB

sch

emat

icfo

ra

bio

nic

sch

emic

alsy

nap

sech

ip

1575 5

4 4

3 3

2 2

1 1

DD

CC

BB

AA

GN

D

OU

T_A

MP

A

OU

T_N

MD

A

GN

D

OU

T_G

AB

AA

GN

D

OU

T_G

AB

AB

GN

D

ELE

AK

EC

H

GN

DG

ND

VB

GN

D

OU

T_G

AB

AB

_P

RE

OU

T_G

AB

AA

_P

RE

OU

T_N

MD

A_P

RE

OU

T_A

MP

A_P

RE

GN

D

OU

T_A

MP

A

GN

D

OU

T_N

MD

A

GN

D

GN

D

OU

T_G

AB

AA

OU

T_G

AB

AB

+6V

+6V

+6V

+6V

OU

T_A

MP

A_P

RE

OU

T_N

MD

A_P

RE

OU

T_G

AB

AA

_P

RE

OU

T_G

AB

AB

_P

RE

GN

D

GN

D

GN

D

GN

D

Buffer of output signal

Unbuffer of output signal

U99

BN

C

U99

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U19

SM

AU

19

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U6

BN

C

U6

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U3

SM

A

U3

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U77

LM

C6001

U77

LM

C6001

BC11

-2

+3

GND4

BC35

OU

T6

VCC7

C28

U70

Test_

Poin

t

U70

Test_

Poin

t

1

U72

Test_

Poin

t

U72

Test_

Poin

t

1

U14

BN

C

U14

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U11

SM

A

U11

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U10

BN

C

U10

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U12

BN

C

U12

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U18

BN

C

U18

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U36

BN

C

U36

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U37

BN

C

U37

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U73

Test_

Poin

t

U73

Test_

Poin

t

1

U76

LM

C6001

U76

LM

C6001

BC11

-2

+3

GND4

BC35

OU

T6

VCC7

C28

U74

Test_

Poin

t

U74

Test_

Poin

t

1

U78

LM

C6001

U78

LM

C6001

BC11

-2

+3

GND4

BC35

OU

T6

VCC7

C28

U75

LM

C6001

U75

LM

C6001

BC11

-2

+3

GND4

BC35

OU

T6

VCC7

C28

U7

SM

A

U7

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U13

SM

A

U13

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U9

SM

A

U9

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U17

BN

C

U17

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U69

Test_

Poin

t

U69

Test_

Poin

t

1

U15

SM

AU

15

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U68

Test_

Poin

t

U68

Test_

Poin

t

1

U2

BN

C

U2

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U71

Test_

Poin

t

U71

Test_

Poin

t

1

U8

BN

C

U8

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

Fig

ure

B.2

:P

CB

sch

emat

icfo

rth

eO

PA

MP

bu

ffer

and

BN

C,

SM

Ap

orts

1585 5

4 4

3 3

2 2

1 1

DD

CC

BB

AA

GN

D

GN

D

I_10nA

I_B

ET

A

GN

D

GN

D

ILE

AK

I0

IB2_1

IB2_1

GN

D

GN

D

GN

D

BIA

S_T

_1

GN

D

BIA

S_T

_2

D100

D200

D400

D800

GN

D

IN_T

_1

IN_T

_2

GN

D

Current sink (positive value)

Ib2 (10nA bias)

VREF to source of ISFET and BIAS_T

Ib1 (Bias current for ref. elec)

IN_T1,2 to

drain of

ISFET

U22

BN

C

U22

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U105

Test_

Poin

t

U105

Test_

Poin

t

1

U109

Test_

Poin

t

U109

Test_

Poin

t

1

U23

SM

AU

23

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U46

JU

MP

ER

2U

46

JU

MP

ER

2

SIG

11

SIG

22

U64

BN

C

U64

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U45

JU

MP

ER

2U

45

JU

MP

ER

2

SIG

11

SIG

22

U50

SM

AU

50

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U114

Test_

Poin

tU

114

Test_

Poin

t

1

U44

JU

MP

ER

2U

44

JU

MP

ER

2

SIG

11

SIG

22

U100

BN

C

U100

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5U

43

JU

MP

ER

2U

43

JU

MP

ER

2

SIG

11

SIG

22

U25

SM

AU

25

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U24

BN

C

U24

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U110

Test_

Poin

t

U110

Test_

Poin

t

1U

47

BN

C

U47

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U49

BN

C

U49

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U48

SM

AU

48

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U104

Test_

Poin

t

U104

Test_

Poin

t

1U

65

BN

C

U65

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U67

BN

C

U67

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U106

Test_

Poin

t

U106

Test_

Poin

t

1

U66

BN

C

U66

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U62

BN

C

U62

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U111

Test_

Poin

t

U111

Test_

Poin

t

1

U58

BN

C

U58

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U21

SM

AU

21

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U103

Test_

Poin

t

U103

Test_

Poin

t

1

U20

BN

C

U20

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U63

BN

C

U63

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U108

Test_

Poin

t

U108

Test_

Poin

t

1

U107

Test_

Poin

tU

107

Test_

Poin

t

1

U4

SM

A

U4

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U102

Test_

Poin

tU

102

Test_

Poin

t

1

U35

SM

AU

35

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U34

BN

C

U34

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

Fig

ure

B.3

:P

CB

sch

emat

icfo

rth

eB

NC

,S

MA

por

tsI

1595 5

4 4

3 3

2 2

1 1

DD

CC

BB

AA

ST

IM_A

MP

A

ST

IM_N

MD

A

ST

IM_G

AB

AA

ST

IM_G

AB

AB

OU

T_T

_1

OU

T_T

_2

SIG

_A

MP

A

SIG

_G

AB

AB

SIG

_N

MD

A

SIG

_G

AB

AA

GN

D

GN

D

GN

D

GN

D

ST

IM_A

MP

A

ST

IM_N

MD

A

ST

IM_G

AB

AA

ST

IM_G

AB

AB

OU

T_T

_1

OU

T_T

_2

GN

D

OUTPUT from TRANS4

SELECT FOR EACH INDIVIDUAL

SYNAPSE#

JUMPER to select

current source for STIM

channel

STIM signal from gen.

Current source

(negative value)

TAP OUT_T for char

U55

JU

MP

ER

2U

55

JU

MP

ER

2

SIG

11

SIG

22

U26

BN

C

U26

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U29

SM

AU

29

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U33

SM

AU

33

SM

A

SIG

1G

12

G2

3

G3

4

G4

5U

60

JU

MP

ER

2U

60

JU

MP

ER

2

SIG

11

SIG

22

U57

JU

MP

ER

2U

57

JU

MP

ER

2

SIG

11

SIG

22

U54

JU

MP

ER

2U

54

JU

MP

ER

2

SIG

11

SIG

22

U53

JU

MP

ER

2U

53

JU

MP

ER

2

SIG

11

SIG

22

U52

JU

MP

ER

2U

52

JU

MP

ER

2

SIG

11

SIG

22

U5

BN

C

U5

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U28

BN

C

U28

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U51

JU

MP

ER

2U

51

JU

MP

ER

2

SIG

11

SIG

22

U27

SM

AU

27

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U31

SM

AU

31

SM

A

SIG

1G

12

G2

3

G3

4

G4

5

U32

BN

C

U32

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U61

JU

MP

ER

2U

61

JU

MP

ER

2

SIG

11

SIG

22

U16

BN

C

U16

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U30

BN

C

U30

BN

C

SIG

1

G1

2

G2

3

G3

4

G4

5

U59

JU

MP

ER

2U

59

JU

MP

ER

2

SIG

11

SIG

22

U56

JU

MP

ER

2U

56

JU

MP

ER

2

SIG

11

SIG

22

Fig

ure

B.4

:P

CB

sch

emat

icfo

rth

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Documents

Bionics Chemical Synapse - Imperial College London · PDF fileBionics Chemical Synapse by Surachoke Thanapitak December 2011 A thesis submitted for the degree of Doctor of Philosophy