Computational dynamics of gradient bistable networks.

We describe a neural-like, homogeneous network consisting of coupled bistable elements and we study its abilities of learning, pattern recognition and computation. The technique allows new possibilities of pattern recognition, including the memorization and perfect recall of several memory patterns, without interference from spurious states. When the coupling strength between elements exceeds a critical value, the network readily converges to a unique attractor. Below this critical value one could perfectly recall all memorized patterns.

[1]  R M Borisyuk,et al.  Memorizing and recalling spatial-temporal patterns in an oscillator model of the hippocampus. , 1998, Bio Systems.

[2]  Seung Kee Han,et al.  TEMPORAL SEGMENTATION OF THE STOCHASTIC OSCILLATOR NEURAL NETWORK , 1998 .

[3]  Chinarov,et al.  Ion pores in biological membranes as self-organized bistable systems. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[4]  E. Izhikevich,et al.  Weakly connected neural networks , 1997 .

[5]  Kemal Leblebicioglu,et al.  Pattern recognition in bistable networks , 1999, Defense, Security, and Sensing.

[6]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[7]  Deliang Wang,et al.  Global competition and local cooperation in a network of neural oscillators , 1995 .

[8]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[9]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[10]  Paul C. Bressloff,et al.  Stochastic dynamics of the diffusive Haken model with subthreshold periodic forcing , 1998 .

[11]  Kevin L. Priddy,et al.  Applications and Science of Computational Intelligence V , 2001 .

[12]  Fernando J. Pineda,et al.  Dynamics and architecture for neural computation , 1988, J. Complex..

[13]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .