Computational Intelligence Hybrid Intelligent System Spiking Neural Network Fuzzy Receptive Neuron Fuzzy Clustering Automatic Control Theory Analog-Digital System Second Order Damped Response System Artificial Intelligence
Institute of Information Theories and Applications FOI ITHEA
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.