Automatic fitting of spiking neuron models to electrophysiological recordings

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Title: Automatic fitting of spiking neuron models to electrophysiological recordings
Author(s): Rossant, C
Goodman, DF
Platkiewicz, J
Brette, R
Item Type: Journal Article
Abstract: Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.
Publication Date: 5-Mar-2010
Date of Acceptance: 2-Feb-2010
ISSN: 1662-5196
Publisher: Frontiers Media
Journal / Book Title: Frontiers in Neuroinformatics
Volume: 4
Copyright Statement: © 2010 Rossant, Goodman, Platkiewicz and Brette. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
Keywords: GPU
adaptive threshold
distributed computing
model fitting
spiking models
Publication Status: Published
Article Number: 2
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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