Automatic fitting of spiking neuron models to electrophysiological recordings
Rossant C, Goodman DFM, Platkiewicz J, Brette R
Frontiers in Neuroinformatics
(2010)
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.
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Related software
A Python simulator for spiking neural networks.
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