Statistical analysis of spike trains under variation of synaptic weights in neuronal networks

Gaia Lombardi 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : The present work is a step forward to improve the statistics of spike trains in a neuron model taking into account the temporal dependence of the neuron response. Based on the minimization of the Kullback-Leibler divergence, parameters of the neuron model such as synaptic weights and external input are adjusted. We first applied the method to data artificially generated with the neuron model and at the end of the internship to experimental data recorded from a real retina in vitro. Clearly, results from the application of the proposed method to the interpretation of real experimental data from a retina in vitro are still preliminary. Nevertheless, they seem to provide an encouraging indication to pursue testing the method with other experimental data of different nature or applying the method to other more complicated neuron models.
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Dynamical Systems [math.DS]. 2014
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Gaia Lombardi. Statistical analysis of spike trains under variation of synaptic weights in neuronal networks. Dynamical Systems [math.DS]. 2014. 〈hal-00954694〉

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