Abstract: Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Keywords: computational intelligence, hybrid intelligent system, spiking neural network, fuzzy receptive neuron, fuzzy clustering, automatic control theory, analog-digital system, second order damped response system.

ACM Classification Keywords: I.2.6 Artificial Intelligence: Learning – Connectionism and neural nets; I.2.8 Artificial Intelligence: Problem Solving, Control Methods, and Search – Control theory; I.5.1 Pattern Recognition: Models – Fuzzy set, Neural nets; I.5.3 Pattern Recognition: Clustering – Algorithms.

Link:

SELF-LEARNING FUZZY SPIKING NEURAL NETWORK AS A NONLINEAR PULSE-POSITION THRESHOLD DETECTION DYNAMIC SYSTEM BASED ON SECOND-ORDER CRITICALLY DAMPED RESPONSE UNITS

Yevgeniy Bodyanskiy, Artem Dolotov, Iryna Pliss

http://foibg.com/ibs_isc/ibs-09/ibs-09-p07.pdf