Dynamics of associative memory with a self-consistent noise

Author

Opris, I.

Author_Institution

Dept. of Phys., Bucharest Univ.

fYear

1995

fDate

31 Aug-2 Sep 1995

Firstpage

162

Lastpage

170

Abstract

The Glauber dynamics of magnetic systems has been extended to the case of neural networks with a general odd response function. The author derives a set of recursion relations for the overlap parameter, noise average and noise variance taken as macrovariables of the process describing the dynamics of associative memory. The retrieval process is studied then for a hyperbolic tangent transfer function by the self-consistent signal to noise ratio method. It takes into account the fatigue effect of the real neuron. The phase diagrams of the retrieval process reveals an enhanced storage capacity for a certain set of parameter values

Keywords

content-addressable storage; dynamics; neural nets; probability; transfer functions; Glauber dynamics; associative memory; enhanced storage capacity; hyperbolic tangent transfer function; magnetic systems; noise average; noise variance; odd response function; recursion relations; retrieval process; self-consistent signal to noise ratio method; Associative memory; Equations; Fatigue; Magnetic noise; Neural networks; Neurons; Signal processing; Signal to noise ratio; Stochastic processes; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-2739-X

Type

conf

DOI

10.1109/NNSP.1995.514890

Filename

514890