Presentation 2003/6/20
Retrieval property of an attractor network with depressing synapses
Daisuke IDE, Narihisa MATSUMOTO, Masataka WATANABE, Masato OKADA,
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Abstract(in English) Neurophysiological experiments show that the strength of synaptic connections can change on a short time scale. These changes depend on the history of presynaptic input. Using SCSNA, we study how synaptic depression influence the performance of attractor neural network in terms of its storage capacity and basins of attraction. We employ a binary discrete-time associative memory model. As a result, the stable retrieval is achieved and the basins of attraction are enlarged. In this work, we show the detailed mechanism in which the synaptic depression enlarges the basins of attraction.
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Keyword(in English) Depressing synapses / Associative memory model / Storage capacity / Basins of attraction / SCSNA
Paper # NC2003-20
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Conference Date 2003/6/20(1days)
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Language JPN
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Title (in English) Retrieval property of an attractor network with depressing synapses
Sub Title (in English)
Keyword(1) Depressing synapses
Keyword(2) Associative memory model
Keyword(3) Storage capacity
Keyword(4) Basins of attraction
Keyword(5) SCSNA
1st Author's Name Daisuke IDE
1st Author's Affiliation Faculty of Engineering, University of Tokyo()
2nd Author's Name Narihisa MATSUMOTO
2nd Author's Affiliation "Intelligent Cooperation and Control", PRESTO, JST
3rd Author's Name Masataka WATANABE
3rd Author's Affiliation Faculty of Engineering, University of Tokyo
4th Author's Name Masato OKADA
4th Author's Affiliation "Intelligent Cooperation and Control", PRESTO, JST:Laboratory for Mathematical Neuroscience, Brain Science Institute, RIKEN
Date 2003/6/20
Paper # NC2003-20
Volume (vol) vol.103
Number (no) 153
Page pp.pp.-
#Pages 6
Date of Issue