Interacting Brownian Particles as a Model of Neural Network

1998 ◽  
Vol 08 (04) ◽  
pp. 791-797 ◽  
Author(s):  
Ichiro Iwasaki ◽  
Hiroshi Nakajima ◽  
Toshihiro Shimizu
Keyword(s):  
Neural Network ◽  
Brownian Motion ◽  
Associative Memory ◽  
Internal State ◽  
Parameter Dependence ◽  
Bifurcation Parameter ◽  
Memory Problem ◽  
Brownian Particles ◽  
Interacting Brownian Particles

A model of neural network is proposed, in which the dynamics of the internal state of neurons is based on the Brownian motion with the chaotic force, and the bifurcation parameter of the chaotic force is modulated by the position of the Brownian particles. This model is applied to the associative memory problem. The parameter dependence of the dynamics is studied. It is shown that the model can retrieve all embedded patterns and reverse patterns successively. It is found that all neurons exhibit synchronized behavior while a pattern is retrieved.

Wandering Motion and Co-Operative Phenomena in a Chaotic Neural Network

1998 ◽  
Vol 08 (05) ◽  
pp. 891-898 ◽  
Author(s):  
Makoto Makishima ◽  
Toshihiro Shimizu
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Discrete Time ◽  
Internal State ◽  
Past History ◽  
Chaotic Neural Network ◽  
Great Ability ◽  
New Model ◽  
The Past

A new model for associative memory with discrete time is proposed, in which chaos is taken into account and moreover the internal state of neurons depends on the past history. The network itself can search for minima of the energy successively by using the wandering motion and co-operative phenomena. By using this model we can clearly discuss the mechanism or the dynamics of the wandering motion and co-operative phenomena in a chaotic neural network to search for minima of the energy. It is shown that our model has great ability in the problem of associative memory.

An associative memory based on an electronic neural network architecture

1987 ◽  
Vol 34 (7) ◽  
pp. 1553-1556 ◽  
Author(s):  
R.E. Howard ◽  
D.B. Schwartz ◽  
J.S. Denker ◽  
R.W. Epworth ◽  
H.P. Graf ◽  
...  
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Network Architecture ◽  
Neural Network Architecture

Diffusion of interacting Brownian particles on non-regularly spaced stepped structures

2013 ◽  
Vol 46 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Youssef Lachtioui ◽  
M’hammed Mazroui ◽  
Yahia Boughaleb ◽  
Elyakoute El Koraychy
Keyword(s):  
Brownian Particles ◽  
Interacting Brownian Particles

scholarly journals A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaofang Hu ◽  
Shukai Duan ◽  
Lidan Wang
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Large Scale ◽  
Cmos Technology ◽  
Chaotic Neural Network ◽  
Circuit Element ◽  
Nanoscale Cmos ◽  
Dynamical Behaviors ◽  
Implementation Scheme ◽  
Further Development

Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1) nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2) it can separate stored patterns from superimposed input; (3) it can deal with one-to-many associative memory; (4) it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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