An information geometrical analysis of neural spike sequences

Authors:
Kazushi Ikeda
Affiliations:
Graduate School of Informatics, Kyoto University, Kyoto, Japan
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

Citing 5
Cited 0

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Attractor reliability reveals deterministic structure in neuronal spike trains

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Differences in spiking patterns among cortical neurons

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Information geometry on hierarchy of probability distributions

IEEE Transactions on Information Theory

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Abstract

Statistical measures for analyzing neural spikes in cortical areas are discussed from the information geometrical viewpoint. Under the assumption that the interspike intervals of a spike sequence of a neuron obey a gamma distribution with a variable spike rate, we formulate the problem of characterization as a semiparametric statistical estimation. We derive an optimal statistical measure under certain assumptions and also show the meaning of some existing measures, such as the coefficient of variation and the local variation.