Stochastic Modeling of Neuronal Responses

Abstract

The sequence of spikes in a train is considered to be a neuronal code. We have shown that a simple model in which the spikes are stochastically thrown down with the probabilities governed by a rate function over time estimated from the spike density over time. The numbers of spikes is determined by measuring the number of spikes over the interval of interest, usually several hundred milliseconds. By analyzing data from this model, which is instantiated by order statistics, neuronal spike trains can be decoded instant-by-instant as they unfold over time.