Self-organized short-term memory mechanism in spiking neural network

Authors:
Mikhail Kiselev
Affiliations:
Megaputer Intelligence Inc., Bloomington, IN
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

Citing 3
Cited 1

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Self-organized spiking neural network recognizing phase/frequency correlations

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks

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

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I

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Abstract

The paper is devoted to implementation and exploration of evolutionary development of the short-term memory mechanism in spiking neural networks (SNN) starting from initial chaotic state. Short-term memory is defined here as a network ability to store information about recent stimuli in form of specific neuron activity patterns. Stable appearance of this effect was demonstrated for so called stabilizing SNN, the network model proposed by the author. In order to show the desired evolutionary behavior the network should have a specific topology determined by "horizontal" layers and "vertical" columns.