Random neural networks with state-dependent firing neurons

Authors:
Sungho Jo;J. Yin;Zhi-Hong Mao
Affiliations:
Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

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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.