NDLI: Random neural networks with state-dependent firing neurons

Content Provider	IEEE Xplore Digital Library
Author	Sungho Jo Yin, J. Zhi-Hong Mao
Copyright Year	1990
Abstract	This letter studies the properties of the random neural networks (RNNs) with state-dependent firing neurons. It is assumed that the times between successive signal emissions of a neuron are dependent on the neuron potential. Under certain conditions, the networks keep the simple product form of stationary solutions and exhibit enhanced capacity of adjusting the probability distribution of the neuron states. It is demonstrated that desired associative memory states can be stored in the networks.
Sponsorship	IEEE Computational Intelligence Society
Page Count	4
File Size	169370
Starting Page	980
Ending Page	983
File Format	PDF
ISSN	10459227
Volume Number	16
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Neurons Recurrent neural networks Biological system modeling Biology computing Biological information theory Capacity planning Probability distribution Associative memory Biological neural networks state-dependent firing rate random neural networks (RNNs) spiking neurons
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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