Abstract
Information geometry studies a family probability distributions by using modern geometry. Since a stochastic model of multiple spike trains is described by a family of probability distributions, information geometry provides not only intuitive understanding, but also useful tools to analyze complex spike trains. A stochastic model of neuronal spikes represents average firing rates and correlations of spikes. We separate correlations of spikes from their firing rates orthogonally. We further separate higher-order correlations from lower-order ones, and thus the effect of correlations is decomposed orthogonally. However, a general model is too complicated and is not adequate for practical use. So we study characteristics of various tractable models. We study among them a mixture model, which is simple and tractable and has many interesting properties. We also study a marginal model and its characteristics.
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Amari, Si. (2010). Information Geometry of Multiple Spike Trains. In: Grün, S., Rotter, S. (eds) Analysis of Parallel Spike Trains. Springer Series in Computational Neuroscience, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5675-0_11
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DOI: https://doi.org/10.1007/978-1-4419-5675-0_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5674-3
Online ISBN: 978-1-4419-5675-0
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