Multiple Stability of a Sparsely Encoded Attractor Neural Network Model for the Inferior Temporal Cortex(General)

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概要

• 論文の詳細を見る

• We study a neural network model for the inferior temporal cortex, in terms of finite memory loading and sparse coding. We show that an uncorrelated Hopfield-type attractor and some correlated attractors have multiple stability, and examine the retrieval dynamics for these attractors when the initial state is set to a noise-degraded memory pattern. Then, we show that there is a critical initial overlap: that is, the system converges to the correlated attractor when the noise level is large, and otherwise to the Hopfield-type attractor. Furthermore, we study the time course of the correlation between the correlated attractors in the retrieval dynamics. On the basis of these theoretical results, we resolve the controversy regarding previous physiologic experimental findings regarding neuron properties in the inferior temporal cortex and propose a new experimental paradigm.

• 社団法人日本物理学会の論文
• 2008-12-15

著者

• OKADA MASATO

  Graduate School of Frontier Sciences, The University of Tokyo

• UEZU Tatsuya

  Graduate School of Humanities and Sciences, Nara Women's University

• Uezu Tatsuya

  Graduate School Of Human Culture Nara Women's University

• KIMOTO Tomoyuki

  Oita National College of Technology

• OKADA Masato

  University of Tokyo

• Okada Masato

  Graduate School Of Frontier Sciences The University Of Tokyo

• Okada Masato

  Division Of Transdisciplinary Of Sciences Graduate School Of Frontier Sciences The University Of Tok

• Okada Masato

  Graduate School Of Engineering Science Osaka University

• Uezu Tatsuya

  Nara Women's Univ. Nara

• OKADA Masato

  Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute:"Intelligent Cooperation and Control", PRESTO, JST, co RIKEN BSI:Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, the University of To

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