Presentation 2007-03-14
Filtering properties of short-term plasticity in the local neural network
Yasunobu IGARASHI, NAOKI Honda, Koichi HASHIMOTO,
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Abstract(in English) We theoretically investigated how short-term plasticity work as a filter in the local neural network. We found a condition where short-term depression played a role of high-pass filter and short-term facilitation did a role of low-pass filter. The condition was that a connection strength of mutual connection is much stronger than that of self connection between excitatory and inhibitory neural populations.
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Keyword(in English) Short-term plasticity (Short-term depression, Short-term facilitation) / Filter / Frequency response / Eigenvalue analysis
Paper # NC2006-126
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Committee NC
Conference Date 2007/3/7(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) Filtering properties of short-term plasticity in the local neural network
Sub Title (in English)
Keyword(1) Short-term plasticity (Short-term depression, Short-term facilitation)
Keyword(2) Filter
Keyword(3) Frequency response
Keyword(4) Eigenvalue analysis
1st Author's Name Yasunobu IGARASHI
1st Author's Affiliation ()
2nd Author's Name NAOKI Honda
2nd Author's Affiliation
3rd Author's Name Koichi HASHIMOTO
3rd Author's Affiliation
Date 2007-03-14
Paper # NC2006-126
Volume (vol) vol.106
Number (no) 588
Page pp.pp.-
#Pages 6
Date of Issue