Random neural networks with state-dependent firing neurons

Author

Jo, Sungho ; Yin, Jijun ; Mao, Zhi-Hong

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA

Volume

16

Issue

4

fYear

2005

fDate

7/1/2005 12:00:00 AM

Firstpage

980

Lastpage

983

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.

Keywords

content-addressable storage; neural nets; probability; associative memory states; probability distribution; random neural network; signal emissions; state dependent firing neurons; stationary solutions; Associative memory; Biological information theory; Biological neural networks; Biological system modeling; Biology computing; Capacity planning; Neural networks; Neurons; Probability distribution; Recurrent neural networks; Associative memory; random neural networks (RNNs); spiking neurons; state-dependent firing rate; Action Potentials; Algorithms; Computer Simulation; Models, Statistical; Neural Networks (Computer);

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2005.849829

Filename

1461439