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Prog. Theor. Phys. Vol. 87 No. 5 (1992) pp. 1119-1126

[ Full Text PDF : FREE ACCESS (471K) ]

Neural Network Model Carrying Phase Information with Application to Collective Dynamics

Yoshiki Kuramoto, Toshio Aoyagi, Ikuko Nishikawa,* Tsuyoshi Chawanya and Koji Okuda

Department of Physics, Kyoto University, Kyoto 606-01
*Division of Intelligence Science, The Graduate School of Science and Technology, Kobe University, Kobe 657

(Received November 19, 1991)

Abstract:

A network of periodically bursting model neurons is proposed. Its unique feature is a complex representation of the cell variables and also of the synaptic matrix. In the strong-coupling limit, the model recovers the traditional neural network model of simple on-off units, while in the weak-coupling limit it reduces to the network of smooth phase oscillators. In the special case of all-to-all excitatory coupling, some numerical and analytical evidence is provided for the occurrence of global phase locking. More complicated collective behavior such as clustering is also discovered numerically. Stimulus-evoked collective oscillations as observed in the cat primary visual cortex are explained within the present framework.


URL : http://ptp.ipap.jp/link?PTP/87/1119/
DOI : 10.1143/PTP.87.1119

[ Full Text PDF : FREE ACCESS (471K) ] Citation:

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  5. Progress of Theoretical Physics Vol. 109 No. 1 (2003) pp. 51-59 :
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