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A Learning Rule of the Oscillatory Neural Networks for InPhase Oscillation
Hiroaki KUROKAWA Chun Ying HO Shinsaku MORI
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E80A
No.9
pp.15851594 Publication Date: 1997/09/25 Online ISSN:
DOI: Print ISSN: 09168508 Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications) Category: Keyword: oscillatory neural network, learning rule, synchronization,
Full Text: PDF(870.6KB)>>
Summary:
This peper proposes a simplified model of the wellknown twoneuron neural oscillator. By eliminating one of the two positive feedback synapses in the neural oscillator, learning for the inphase control of the oscillator is shown to be achievable via a very simple learning rule. The learning rule is devised in such a way that only the plasticity of two synaptic weights are required. We demonstrate some examples of the synchronization learning to validate the efficiency of the learning rule, and finally by illustrating the dynamics of the synchronization learning and by using computer simulation, we show the convergence behavior and the stability of the learning rule for the twoneuron simple neural oscillator.

