Presentation 2007-07-24
A Bayesian Analysis of the Time Dependent Irregularity of Neuronal Firing
Takeaki SHIMOKAWA, Shigeru SHINOMOTO,
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Abstract(in English) We have recently proposed a coefficient of local variation, termed Lv, for measuring the irregularity of neuronal firing. Cortical neurons have been classified into several firing types according to the values of Lv. This could be performed by confirming that the temporal variation of Lv for each neuron is small compared to the variation among neurons. We examine the temporal change of the firing irregularity by means of a Bayesian analysis: In our new framework, the rate and irregularity of the spikes are assumed to be slowly changing in time, and are determined under the Bayes method, given the spike data. We are going to apply this Bayes method to experimentally recorded spike trains by focusing on the degree of irregularity fluctuation.
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Keyword(in English) neural spike trains / irregularity / Bayes estimation / point process / gamma process
Paper # NC2007-26
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Committee NC
Conference Date 2007/7/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Bayesian Analysis of the Time Dependent Irregularity of Neuronal Firing
Sub Title (in English)
Keyword(1) neural spike trains
Keyword(2) irregularity
Keyword(3) Bayes estimation
Keyword(4) point process
Keyword(5) gamma process
1st Author's Name Takeaki SHIMOKAWA
1st Author's Affiliation Graduate School of Science, Kyoyo University()
2nd Author's Name Shigeru SHINOMOTO
2nd Author's Affiliation Graduate School of Science, Kyoyo University
Date 2007-07-24
Paper # NC2007-26
Volume (vol) vol.107
Number (no) 157
Page pp.pp.-
#Pages 4
Date of Issue