Stability Analysis of Stochastic Neural Network with Depression and Facilitation Synapses
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概要
- 論文の詳細を見る
We investigate dynamical properties of a stochastic neural network model in which neurons are connected by dynamic synapses that undergo short-term depression and facilitation. In this model, the state of the neuron is described by a binary variable that represents the active or resting state of the neuron and changes stochastically. Synaptic transmission efficacy is described by variables that represent the releasable neurotransmitters and the calcium concentration of the synaptic terminal. Here, we focus on a neural network with uniform connections, and we elucidate its neural dynamics, which is influenced by dynamic synapses. We derive a macroscopic mean field model that approximates the overall behavior of the stochastic neural network. We apply stability and bifurcation analyses to the macroscopic mean field model, and we find that the network exhibits a variety of dynamical structures, including ferromagnetic and paramagnetic states, as well as an oscillatory uniform state according to the parameters that specify the noise intensity and the properties of the dynamic synapses. We believe that the present study can be a potential milestone for future modeling studies on various structured networks with dynamic synapses.
- 2012-11-15
著者
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Aihara Kazuyuki
Institute Of Industrial Science The University Of Tokyo
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Okada Masato
Graduate School Of Engineering Science Osaka University
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Okada Masato
Graduate School of Frontier Science, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
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Igarashi Yasuhiko
Graduate School of Frontier Science, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
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Katori Yuichi
FIRST, Aihara Innovative Mathematical Modelling Project, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
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Aihara Kazuyuki
Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo 153-8505, Japan
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