Self-organization process in large spiking neural networks leading to formation of working memory mechanism

Authors:
Mikhail Kiselev
Affiliations:
Megaputer Intelligence Ltd., Moscow
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

Citing 3
Cited 0

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ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I

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Abstract

The subject of this work is evolutionary process in initially chaotic and homogenous spiking neural networks leading to formation of the neuron groups with partially synchronized activity (so called polychronous groups) which are not only capable of recognizing input patterns but also can keep information about pattern presentation in form of their specific activity for a long time. This result is demonstrated for very simple neuron --- coincidence detector and for standard synaptic plasticity model (STDP).