Soft-Reward Based Reinforcement Learning by Spiking Neural Networks

Abstract:

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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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Price:

$35.00

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