Stochastic Transitions of Attractors in Associative Memory Models with Correlated Noise(Condensed matter: structure and mechanical and thermal properties)
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概要
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We investigate dynamics of recurrent neural networks with correlated noise to analyze the noise's effect. The mechanism of correlated firing has been analyzed in various models, but its functional roles have not been discussed in sufficient detail. Aoyagi and Aoki have shown that the state transition of a network is invoked by synchronous spikes. We introduce two types of noise to each neuron: thermal independent noise and correlated noise. Due to the effects of correlated noise, the correlation between neural inputs cannot be ignored, so the behavior of the network has sample dependence. We discuss two types of associative memory models: one with auto- and weak cross-correlation connections and one with hierarchically correlated patterns. The former is similar in structure to Aoyagi and Aoki's model. We show that stochastic transition can be presented by correlated rather than thermal noise. In the latter, we show stochastic transition from a memory state to a mixture state using correlated noise. To analyze the stochastic transitions, we derive a macroscopic dynamic description as a recurrence relation form of a probability density function when the correlated noise exists. Computer simulations agree with theoretical results.
- 社団法人日本物理学会の論文
- 2006-12-15
著者
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OKADA Masato
Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The Universi
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Kawamura Masaki
Graduate School Of Science And Engineering Yamaguchi University
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Kawamura Masaki
Yamaguchi Univ. Yamaguchi
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Okada Masato
Department Of Complexity Science And Engineering Graduate School Of Frontier Sciences The University
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Okada Masato
Department Of Complexity Science And Engineering Graduate School Of Frontier Sciences The University
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Okada Masato
Department Of Bioscience Tokyo University Of Agriculture
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