Information geometry in neural spike sequences

Abstract

An information geometrical method is developed for characterizing or classifying neurons in cortical areas whose spike rates fluctuate in time. The interspike intervals (ISIs) of a spike sequence of a neuron is modeled as a gamma process with a time-variant spike rate, a fixed shape parameter and a fixed absolute refractory period. We formulate the problem of estimating the fixed parameters as semiparametric estimation and apply an information geometrical method to derive the optimal estimators from a statistical viewpoint.

Keywords

information geometry / neural spikes / semiparametric estimation / interspike intervals (ISIs)

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Higher Education Press and Springer-Verlag Berlin Heidelberg