ANALOG-DIGITAL SELF-LEARNING FUZZY SPIKING NEURAL NETWORK AND ITS LEARNING ALGORITHM BASED ON ‘WINNER-TAKES-MORE’ RULE

Authors

  • Ye. Bodyanskiy
  • A. Dolotov

DOI:

https://doi.org/10.15588/1607-3274-2010-1-20

Keywords:

analog-digital architecture, self-learning fuzzy spiking neural network, automatic control theory, unsupervised learning algorithm, ‘winner-takes-more’ rule.

Abstract

Analog-digital architecture of self-learning fuzzy spiking neural network is proposed in this paper.
Spiking neuron synapse and some are treated in terms of classical automatic control theory.
Conventional unsupervised learning algorithm of spiking neural network is improved by applying
‘Winner-Takes-More’ rule.

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Published

2010-05-15

How to Cite

Bodyanskiy, Y., & Dolotov, A. (2010). ANALOG-DIGITAL SELF-LEARNING FUZZY SPIKING NEURAL NETWORK AND ITS LEARNING ALGORITHM BASED ON ‘WINNER-TAKES-MORE’ RULE. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2010-1-20

Issue

No. 1 (2010): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2014 Ye. Bodyanskiy, A. Dolotov

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