Self-organization of day cycle and hierarchical associative memory in “live” neural network

1992 ◽  
Vol 67 (1) ◽  
pp. 73-81 ◽  
Author(s):  
L. B. Emelyanov-Yaroslavsky ◽  
V. I. Potapov
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Self Organization

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

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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