Presentation 1999/7/23
Linearization by noise in neural model
Takayuki TAKAHATA, Tetsuya SHIMOKAWA, Seiji TANABE, Khashayar PAKDAMAN, Shunsuke SATO,
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Abstract(in English) We examine linearization by noise is periodically forced leaky integrate-and-fire neuron models. When the stimulus is a single sine, the cycle histogram takes on a sine like shape as the noise intensity is increased. This form of linearizaion is in agreement with experimental recordings. We extend these by analyzing the response of the model to a periodic signal composed of two sines, and in particular we examine whether the superposition principle holds.
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Keyword(in English) leaky integrate-and-fire model / noise / super-position principle
Paper # CST99-27
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Committee CST
Conference Date 1999/7/23(1days)
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Registration To Concurrent System Technology (CST)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Linearization by noise in neural model
Sub Title (in English)
Keyword(1) leaky integrate-and-fire model
Keyword(2) noise
Keyword(3) super-position principle
1st Author's Name Takayuki TAKAHATA
1st Author's Affiliation Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University()
2nd Author's Name Tetsuya SHIMOKAWA
2nd Author's Affiliation Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University
3rd Author's Name Seiji TANABE
3rd Author's Affiliation Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University
4th Author's Name Khashayar PAKDAMAN
4th Author's Affiliation Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University
5th Author's Name Shunsuke SATO
5th Author's Affiliation Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University
Date 1999/7/23
Paper # CST99-27
Volume (vol) vol.99
Number (no) 207
Page pp.pp.-
#Pages 7
Date of Issue