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Shigeru Shinomoto

Researcher at Kyoto University

Publications -  111
Citations -  4117

Shigeru Shinomoto is an academic researcher from Kyoto University. The author has contributed to research in topics: Spike (software development) & Spike train. The author has an hindex of 28, co-authored 109 publications receiving 3676 citations. Previous affiliations of Shigeru Shinomoto include Tohoku University & Nippon Telegraph and Telephone.

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Journal ArticleDOI

A Method for Selecting the Bin Size of a Time Histogram

TL;DR: A method for objectively selecting the bin size from the spike count statistics alone, so that the resulting bar or line graph time histogram best represents the unknown underlying spike rate.
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Kernel bandwidth optimization in spike rate estimation

TL;DR: It is revealed that the classical kernel smoother may exhibit goodness-of-fit comparable to, or even better than, that of modern sophisticated rate estimation methods, provided that the bandwidth is selected properly for a given set of spike data, according to the optimization methods presented here.
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Phase transitions in active rotator systems

TL;DR: In this paper, the authors introduce a modele de rotateur actif afin d'etudier la dynamique statistique d'une grande population doscillateurs a cycle limite ou d'elements excitables.
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Relating neuronal firing patterns to functional differentiation of cerebral cortex.

TL;DR: Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas, random in the visual areas, and bursty in the prefrontal area, suggesting that signaling patterns may play an important role in function-specific cerebral cortical computation.
Journal ArticleDOI

Differences in spiking patterns among cortical neurons

TL;DR: It is found that a measure of the local variation of interspike intervals, LV, is nearly the same for every spike sequence for any given neuron, while it varies significantly among neurons.