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- 伊達 章
- 大阪大学基礎工学部生物工学科
書誌事項
- タイトル別名
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- Properties of Neuronal Model Dependence in the Randomly and Symmetrically Connected Neural Networks
- ランダム タイショウ ケツゴウ シンケイ カイロモウ ノ シンケイ サイボウ
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説明
A large number of equilibrium states or fixed points is in a randomly and symmetrically connected neural network. Recently it has been shown that the maximum number which can be realized depend on the model of the single neuron. Here we show some network properites of the neuronal model dependence which include the maximum number of equibrium states and the activity of these states. Furthermore, the invariant activity in each model is also derived, where the activity does not depend on the statistical parameters designated by the probability distribution of connection weights between neurons and a threshold of neurons.
収録刊行物
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- 日本応用数理学会論文誌
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日本応用数理学会論文誌 7 (2), 97-106, 1997
一般社団法人 日本応用数理学会
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詳細情報 詳細情報について
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- CRID
- 1390001205768015488
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- NII論文ID
- 110001883646
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- NII書誌ID
- AN10367166
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- ISSN
- 09172246
- 24240982
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- NDL書誌ID
- 4236483
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可