Nonlinear Sciences – Adaptation and Self-Organizing Systems
Scientific paper
1994-11-30
Nonlinear Sciences
Adaptation and Self-Organizing Systems
7 pages, compressed and uuencoded postscript file
Scientific paper
In this paper, we investigate the associative memory in recurrent neural networks, based on the model of evolving neural networks proposed by Nolfi, Miglino and Parisi. Experimentally developed network has highly asymmetric synaptic weights and dilute connections, quite different from those of the Hopfield model. Some results on the effect of learning efficiency on the evolution are also presented.
Fujita Sh.
Nishimura Harumichi
No associations
LandOfFree
An Evolutionary Approach to Associative Memory in Recurrent Neural Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with An Evolutionary Approach to Associative Memory in Recurrent Neural Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An Evolutionary Approach to Associative Memory in Recurrent Neural Networks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-308981