Presentation 2002/3/11
Bistability of Attractor Neural Network Model for Inferior Temporal Cortex
Masato OKADA, Aya HIRANO, Tatsuya UEZU,
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Abstract(in English) We study a neural network model for inferior temporal cortex proposed by Griniasty et al. in the finite memory loading. We show that uncorrelated Hopfield type and 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 / Bistability / Inferior temporal cortex / Stimulus selectivity
Paper # NC2001-136
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Committee NC
Conference Date 2002/3/11(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Bistability of Attractor Neural Network Model for Inferior Temporal Cortex
Sub Title (in English)
Keyword(1) Associative memory model
Keyword(2) Attractor
Keyword(3) Bistability
Keyword(4) Inferior temporal cortex
Keyword(5) Stimulus selectivity
1st Author's Name Masato OKADA
1st Author's Affiliation RIKEN Brain Science Institute()
2nd Author's Name Aya HIRANO
2nd Author's Affiliation Graduate School of Human Culture, Nara Women's University
3rd Author's Name Tatsuya UEZU
3rd Author's Affiliation Graduate School of Human Culture, Nara Women's University
Date 2002/3/11
Paper # NC2001-136
Volume (vol) vol.101
Number (no) 735
Page pp.pp.-
#Pages 8
Date of Issue