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Open AccePoster presentationDatabase analysis and visualization of simulated and recorded electrophysiological data with PANDORA's Toolbox in MatlabCengiz Günay* and Dieter Jaeger

Address: Biology Department, Emory University, Atlanta

Email: Cengiz Günay* - cgunay@emory.edu

* Corresponding author

IntroductionWe developed a generic database-supported analysis andvisualization software, PANDORA, for research projectswith large datasets and many parameters. PANDORA sup-ports analyzing electrophysiological data from intracellu-lar recordings and from computer simulations ofcompartmental neuron models. An easy querying systemmatches the power of SQL databases, while the Matlabcomputing environment provides specialized numericprocessing, analysis and visualization tools. PANDORAcan be used for organizing and keeping track of complexphysiological (e.g., recording channel, stimulus magni-tude) or computer simulation (e.g., ion channel densities,kinetics, compartment lengths) parameters of a singleneuron or network model. The PANDORA toolbox can befreely obtained from http://userwww.service.emory.edu/~cgunay/pandora.

MotivationThe amount of electrophysiological data is increasing asmore channels can be sampled and recording qualityimproves, while rapid advances in computing speed andcapacity have enabled researchers to generate massiveamounts of simulation data in very short times. Recently,interesting results were obtained from modeling studieswith such large datasets [1,2].

Although widely used for identified neurons and brainconnectivity [3], databases are rarely used in electrophysi-ological analysis [4]. The main advantage of using a data-base is being able to associate metadata labels with rawdata for querying and organizing the data based on the

information in these labels. For example, automatic labe-ling of control and drug applied recordings in a databasereduces risk of analysis errors.

ResultsPANDORA offered specific improvements in analyzingelectrophysiological data. Neuroscientists conventionallyprefer qualitative analysis of raw data traces. However, asthe amount of collected data increases, it is more desirableto have quantitative results. PANDORA offers an auto-mated way of extracting measurements defined by theelectrophysiologist. It was more efficient to storage, searchand analyze these measurements and to find associatedexperimental or simulation parameters. Furthermore,PANDORA kept pointers back to raw data that allowedverifying results obtained from this high-level analysis.

Electrophysiological data sometimes needs to be analyzedat different levels of abstraction. At the lower level, multi-ple traces collected from one neuron must be displayedand analyzed, while to understand effects across neurons,one must look at summary information from each neu-ron. PANDORA routines that allow one to sift, averageand collapse parameter dimensions were essential inswitching between these levels of abstraction.

For large simulation projects searching model parameters,PANDORA offered several routines to understand theeffects, on the measured characteristics, of a single param-eter while other parameters were invariant. The resultscould then be subjected to second tier analyses such asderivative and correlation, or simply be plotted.

from Seventeenth Annual Computational Neuroscience Meeting: CNS*2008Portland, OR, USA. 19–24 July 2008

Published: 11 July 2008

BMC Neuroscience 2008, 9(Suppl 1):P82 doi:10.1186/1471-2202-9-S1-P82

This abstract is available from: http://www.biomedcentral.com/1471-2202/9/S1/P82

© 2008 Günay and Jaeger; licensee BioMed Central Ltd.

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In summary, PANDORA was aimed to make analysis ofelectrophysiological data easier and provides a flexibleplatform for standardized analysis.

AcknowledgementsThis work was supported by NINDS R01-NS039852 and NIMH R01-MH065634.

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