Neural Network Model with Discrete and Continuous Information Representation
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- Kitazono Jun
- Graduate School of Frontier Sciences, The University of Tokyo
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- Omori Toshiaki
- Graduate School of Frontier Sciences, The University of Tokyo RIKEN Brain Science Institute
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- Okada Masato
- Graduate School of Frontier Sciences, The University of Tokyo RIKEN Brain Science Institute
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An associative memory model and a neural network model with a Mexican-hat type interaction are two major attractor neural network models. The associative memory model has discretely distributed fixed-point attractors, and achieves a discrete information representation. On the other hand, a neural network model with a Mexican-hat type interaction uses a ring attractor to achieves a continuous information representation, which can be seen in the working memory in the prefrontal cortex and columnar activity in the visual cortex. In the present study, we propose a neural network model that achieves discrete and continuous information representation. We use a statistical–mechanical analysis to find that a localized retrieval phase exists in the proposed model, where the memory pattern is retrieved in the localized subpopulation of the network. In the localized retrieval phase, the discrete and continuous information representation is achieved by using the orthogonality of the memory patterns and the neutral stability of fixed points along the positions of the localized retrieval. The obtained phase diagram suggests that the antiferromagnetic interaction and the external field are important for generating the localized retrieval phase.
収録刊行物
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- Journal of the Physical Society of Japan
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Journal of the Physical Society of Japan 78 (11), 114801-114801, 2009
一般社団法人 日本物理学会
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詳細情報 詳細情報について
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- CRID
- 1390282679173584128
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- NII論文ID
- 130005437261
- 40016829650
- 210000108090
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- NII書誌ID
- AA00704814
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- ISSN
- 13474073
- 00319015
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- NDL書誌ID
- 10439436
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可