PATTERN RECOGNITION WITH STOCHASTIC RESONANCE IN A GENERIC NEURAL NETWORK

Z. Tan () and M. K. Ali ()
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Z. Tan: Department of Physics, The University of Lethbridge, 4401 University Dr. W Lethbridge, Alberta T1K 3M4, Canada
M. K. Ali: Department of Physics, The University of Lethbridge, 4401 University Dr. W Lethbridge, Alberta T1K 3M4, Canada

International Journal of Modern Physics C (IJMPC), 2000, vol. 11, issue 08, 1585-1593

Abstract: We discuss stochastic resonance in associative memory with a canonical neural network model that describes the generic behavior of a large family of dynamical systems near bifurcation. Our result shows that stochastic resonance helps memory association. The relationship between stochastic resonance, associative memory, storage load, history of memory and initial states are studied. In intelligent systems like neural networks, it is likely that stochastic resonance combined with synaptic information enhances memory recalls.

Keywords: Pattern Recognition; Associative Memory; Stochastic Resonance; Neural Networks (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1142/S0129183100001413

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