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.
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Ikeda, K. (2005). An Information Geometrical Analysis of Neural Spike Sequences. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_22
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DOI: https://doi.org/10.1007/11550822_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
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