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Original Research ARTICLE

Automatic fitting of spiking neuron models to electrophysiological recordings

Cyrille Rossant^1,2, Dan F. M. Goodman^1,2, Jonathan Platkiewicz^1,2 and Romain Brette^1,2*

Laboratoire Psychologie de la Perception, Centre National de la Recherche Scientifique and Université Paris Descartes, Paris, France

Département d’Etudes Cognitives, Ecole Normale Supérieure, Paris, France

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.

Keywords:

model fitting, electrophysiology, spiking models, simulation, GPU, distributed computing, adaptive threshold, optimization

Citation:

Rossant C, Goodman DFM, Platkiewicz J and Brette R (2010). Automatic fitting of spiking neuron models to electrophysiological recordings. Front. Neuroinform. 4:2. doi: 10.3389/neuro.11.002.2010

Received:

17 December 2009;

Paper pending published:

12 January 2010;

Accepted:

02 February 2010;

Published online:

05 March 2010

Edited by:

Erik De Schutter, University of Antwerp, Belgium; Okinawa Institute of Science and Technology, Japan

Reviewed by:

Werner Van Geit, Okinawa Institute of Science and Technology, Japan;
Astrid A. Prinz, Emory University, USA;
Magnus Richardson, University of Warwick, UK

© 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.

*Correspondence:

Romain Brette, Equipe Audition, Département d’Etudes Cognitives, Ecole Normale Supérieure, 29, rue d’Ulm, 75230 Paris Cedex 05, France. e-mail: romain.brette@ens.fr