Presentation 2005/11/12
Multiple stability of Attractor Neural Network Model for Inferior Temporal Cortex : Case of Sparse Cording
Tomoyuki KIMOTO, Tatsuya UEDZU, Masato OKADA,
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Abstract(in English) We study a neural network model for inferior temporal cortex proposed by Amit et al. in the finite memory loading and sparase coding. We show that uncorrelated Hopfield type and some correlated attractors coexist, and examine the retrieval processes for these attractors when the initial state is set to a noisy-degraded stored pattern. We found that there exists a critical value of initial overlap, that is, the system converges to the correlated attractor when the degree of noise is large, otherwise to the Hopfield type attractor. We resolve controversy of previously obtained experimental findings regarding neuron properties in the inferior temporal cortex, and propose a novel experimental paradigm based on these theoretical results.
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Keyword(in English) Associative memory model / Attractor / Multiple stability / Inferior temporal cortex / Stimulus selectivity
Paper # NC2005-75
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Committee NC
Conference Date 2005/11/12(1days)
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Registration To Neurocomputing (NC)
Language JPN
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Title (in English) Multiple stability of Attractor Neural Network Model for Inferior Temporal Cortex : Case of Sparse Cording
Sub Title (in English)
Keyword(1) Associative memory model
Keyword(2) Attractor
Keyword(3) Multiple stability
Keyword(4) Inferior temporal cortex
Keyword(5) Stimulus selectivity
1st Author's Name Tomoyuki KIMOTO
1st Author's Affiliation Oita National College of Technology()
2nd Author's Name Tatsuya UEDZU
2nd Author's Affiliation Graduate School of Human Culture, Nara Women's University
3rd Author's Name Masato OKADA
3rd Author's Affiliation Graduate School of Frontier Sciences, University of Tokyo
Date 2005/11/12
Paper # NC2005-75
Volume (vol) vol.105
Number (no) 419
Page pp.pp.-
#Pages 6
Date of Issue