S
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Shigeru Shinomoto,Hideaki Kim,Takeaki Shimokawa,Nanae Matsuno,Shintaro Funahashi,Keisetsu Shima,Ichiro Fujita,Hiroshi Tamura,Taijiro Doi,Kenji Kawano,Naoko Inaba,Kikuro Fukushima,Sergei Kurkin,Kiyoshi Kurata,Masato Taira,Ken Ichiro Tsutsui,Hidehiko Komatsu,Tadashi Ogawa,Kowa Koida,Jun Tanji,Keisuke Toyama +20 more
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.