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Estimating the interaction graph of stochastic neural dynamics

In this talk we address the question of statistical model selection for a class of stochastic models for biological neural nets. Models in this class are systems of interacting chains with memory of variable length. Each chain describes the activity of a single neuron, indicating whether it has a spike or not at a given time. For each neuron, the probability of having a spike depends on the entire time evolution of its presynaptic neurons since the last spike time of the neuron. When the neuron spikes, its potential is reset to a resting level, and all of its postsynaptic neurons receive an additional amount of potential. The relationship between a neuron and its pre- and postsynaptic neurons defines an oriented graph, the interaction graph of the model. The goal of this talk is to present a consistent selection procedure to estimate this graph of interactions, based on an observation of the process up to time n, within a growing sequence of observation windows.

CMAP UMR 7641 École Polytechnique CNRS, Route de Saclay, 91128 Palaiseau Cedex France, Tél: +33 1 69 33 46 23 Fax: +33 1 69 33 46 46