Shapes of nonmonotonic activation functions in a chaotic neural network associative memory model and its evaluation

2008 ◽  
Vol 91 (3) ◽  
pp. 22-27 ◽  
Author(s):  
Masanao Obayashi ◽  
Rie Omiya ◽  
Takashi Kuremoto ◽  
Kunikazu Kobayashi
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Memory Model ◽  
Activation Functions ◽  
Chaotic Neural Network

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.

Chaotic neural network for associative memory

1999 ◽  
Vol 16 (2) ◽  
pp. 130-137
Author(s):  
Yifeng Zhang ◽  
Luxi Yang ◽  
Zhenya He
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Chaotic Neural Network

Associative memory with a controlled chaotic neural network

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2794-2805 ◽  
Author(s):  
Guoguang He ◽  
Luonan Chen ◽  
Kazuyuki Aihara
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Chaotic Neural Network

scholarly journals HOW A PEAK CAN BE SEARCHED FOR IN AN ALMOST EVERYWHERE FLATLAND OF ALTITUDE ZERO? — TINY FLAT ISLAND IN HUGE LAKE

2014 ◽  
pp. 32-37
Author(s):  
Akira Imada
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Configuration Space ◽  
Fitness Landscape ◽  
Spiking Neurons ◽  
The Other ◽  
Memory Model ◽  
Almost Everywhere

We are exploring a weight configuration space searching for solutions to make our neural network with spiking neurons do some tasks. For the task of simulating an associative memory model, we have already known one such solution — a weight configuration learned a set of patterns using Hebb’s rule, and we guess we have many others which we have not known so far. In searching for such solutions, we observed that the so-called fitness landscape was almost everywhere completely flatland of altitude zero in which the Hebbian weight configuration is the only unique peak, and in addition, the sidewall of the peak is not gradient at all. In such circumstances how could we search for the other peaks? This paper is a call for challenges to the problem.

Sign in / Sign up

Export Citation Format

Share Document