Fitting neuron models to spike trains

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Title: Fitting neuron models to spike trains
Author(s): Rossant, C
Goodman, DF
Fontaine, B
Platkiewicz, J
Magnusson, AK
Brette, R
Item Type: Journal Article
Abstract: Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model.
Publication Date: 23-Feb-2011
Date of Acceptance: 13-Jan-2011
URI: http://hdl.handle.net/10044/1/40626
DOI: http://dx.doi.org/10.3389/fnins.2011.00009
ISSN: 1662-4548
Publisher: Frontiers Media
Journal / Book Title: Frontiers in Neuroscience
Volume: 5
Copyright Statement: © 2011 Rossant, Goodman, Fontaine, Platkiewicz, Magnusson and Brette. This is an open-access article subject to an exclusive license agreement between the authors and Frontiers Media SA, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
Keywords: optimization
parallel computing
python
simulation
spiking models
1109 Neurosciences
1702 Cognitive Science
Publication Status: Published
Article Number: 9
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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