Paper Title:
Soft-Reward Based Reinforcement Learning by Spiking Neural Networks
  Abstract

In this paper, we propose algorithm based reinforcement learning for spiking neural networks. The algorithm simulates biological adaptability and uses the soft-reward from environment to modulate the synaptic weight, which combines spike-timing-dependent plasticity (STDP), winner-take-all mechanism. The algorithm is tested to classify a number of standard benchmark dataset. The obtained results show the effectiveness of the proposed algorithm.

  Info
Periodical
Advanced Materials Research (Volumes 219-220)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
770-773
DOI
10.4028/www.scientific.net/AMR.219-220.770
Citation
W. Y. Shi, "Soft-Reward Based Reinforcement Learning by Spiking Neural Networks", Advanced Materials Research, Vols. 219-220, pp. 770-773, 2011
Online since
March 2011
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