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Innovative Methodology

Improved Spike-Sorting By Modeling Firing Statistics and Burst-Dependent Spike Amplitude Attenuation: A Markov Chain Monte Carlo Approach

¹Laboratoire de Physiologie Cérébrale, Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche (UMR) 8118, Université René Descartes, 75006, Paris; ²Laboratoire de Physique Théorique des Liquides, CNRS UMR 7600, Université Pierre et Marie Curie, 75252 Paris Cedex 05; and ³Laboratoire d'Analyse et de Mathématiques Appliquées, CNRS UMR 8050, Université de Marne la Vallée, Batiment Copernic, Cité Descartes, 77454 Marne la Vallée Cedex, France

Submitted 10 March 2003; accepted in final form 14 January 2004

Spike-sorting techniques attempt to classify a series of noisy electrical waveforms according to the identity of the neurons that generated them. Existing techniques perform this classification ignoring several properties of actual neurons that can ultimately improve classification performance. In this study, we propose a more realistic spike train generation model. It incorporates both a description of "nontrivial" (i.e., non-Poisson) neuronal discharge statistics and a description of spike waveform dynamics (e.g., the events amplitude decays for short interspike intervals). We show that this spike train generation model is analogous to a one-dimensional Potts spin-glass model. We can therefore tailor to our particular case the computational methods that have been developed in fields where Potts models are extensively used, including statistical physics and image restoration. These methods are based on the construction of a Markov chain in the space of model parameters and spike train configurations, where a configuration is defined by specifying a neuron of origin for each spike. This Markov chain is built such that its unique stationary density is the posterior density of model parameters and configurations given the observed data. A Monte Carlo simulation of the Markov chain is then used to estimate the posterior density. We illustrate the way to build the transition matrix of the Markov chain with a simple, but realistic, model for data generation. We use simulated data to illustrate the performance of the method and to show that this approach can easily cope with neurons firing doublets of spikes and/or generating spikes with highly dynamic waveforms. The method cannot automatically find the "correct" number of neurons in the data. User input is required for this important problem and we illustrate how this can be done. We finally discuss further developments of the method.

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