Presentation 2009-07-14
Analysis for Stability of Attractor Neural Network Model for IT Cortex : Relation between attractor's stability and learning order
Tomoyuki KIMOTO, Tatsuya UEZU, Masato OKADA,
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Abstract(in English) In the model proposed by Amit et al. to explain the Miyashita's physiologic findings, the attractors that are mixtures of consecutively learned memory patterns become equilibrium states. It is thought that it is significant to store the relation information between the memory patterns as the correlated attractor in the brain, since the memory patterns continuously observed must be had mutually relation. On the other hand, when it is continuously observed the memory patterns coded the object images accidentally located in neighborhood, it is necessary to store each memory patterns individually. In this study, we examined the attractor's stability in the Amit model, when the learning order of the memory patterns gradually changes from same order to random presentation. As the result, we found that the acquired attractors change into the memory patterns from the correlated attractors via the the symmetrical mixed sates.
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Keyword(in English) IT-cortex / Miyashita's physiologic experiment / Amit's model / correlated attractor / learning order / stability
Paper # NLP2009-39,NC2009-32
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Committee NC
Conference Date 2009/7/6(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Analysis for Stability of Attractor Neural Network Model for IT Cortex : Relation between attractor's stability and learning order
Sub Title (in English)
Keyword(1) IT-cortex
Keyword(2) Miyashita's physiologic experiment
Keyword(3) Amit's model
Keyword(4) correlated attractor
Keyword(5) learning order
Keyword(6) stability
1st Author's Name Tomoyuki KIMOTO
1st Author's Affiliation Oita National College of Technology()
2nd Author's Name Tatsuya UEZU
2nd Author's Affiliation Graduate School of Sciences and Humanities, Nara Women's University
3rd Author's Name Masato OKADA
3rd Author's Affiliation Graduate School of Frontier Sciences, University of Tokyo:RIKEN Brain Science Institute
Date 2009-07-14
Paper # NLP2009-39,NC2009-32
Volume (vol) vol.109
Number (no) 125
Page pp.pp.-
#Pages 6
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