NEURAL NETWORK MODEL CARRYING PHASE INFORMATION WITH APPLICATION TO COLLECTIVE DYNAMICS

被引：22

作者：

KURAMOTO, Y ^{[1
]}

AOYAGI, T ^{[1
]}

NISHIKAWA, I ^{[1
]}

CHAWANYA, T ^{[1
]}

OKUDA, K ^{[1
]}

机构：

[1] KOBE UNIV,GRAD SCH SCI & TECHNOL,DIV INTELLIGENCE SCI,KOBE 657,JAPAN

来源：

PROGRESS OF THEORETICAL PHYSICS | 1992年 / 87卷 / 05期

关键词：

D O I：

10.1143/PTP.87.1119

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页码：1119 / 1126

页数：8

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