Learning Methods of Recurrent Spiking Neural Networks

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  • SELVARATNAM Kukan
    Department of Electronics and Information Science, Kyoto Institute of Technology
  • KUROE Yasuaki
    Department of Electronics and Information Science, Kyoto Institute of Technology
  • MORI Takehiro
    Department of Electronics and Information Science, Kyoto Institute of Technology

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Other Title
  • リカレントスパイキングニューラルネットワークの学習法
  • リカレントスパイキングニューラル ネットワーク ノ ガクシュウホウ カト オヨビ シンドウセイ ノ スパイクレツ ノ ガクシュウ
  • Transient and Oscillatory Spike Trains
  • 過渡および振動性のスパイク列の学習

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Abstract

In an artificial Spiking Neural Network (SNN) the information processing and transmission are carried out by spike trains in a manner similar to the generic biological neurons. Recently it has been reported that they are computationally more powerful than the conventional neural networks. Yet, there are no well defined efficient methods for learning due to their rather intricately discontinuous and nonlinear mechanisms. In this paper, we consider a recurrent SNN constructed with integrate-and-fire type spiking neurons. First we propose a learning method such that the SNN possesses desired transient responses (spike-train outputs) by changing the synaptic weights. Further by including periodic state conditions we propose a learning method such that the SNN possesses desired oscillatory responses (limit cycle spike train) by changing both the synaptic weights and the initial conditions. Simulation examples are also provided to verify the efficiency and the applicability of the proposed algorithm.

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