An Evolutionary Approach to Associative Memory in Recurrent Neural   Networks
  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. 
Sh. Fujita (Kobe University) and H. Nishimura (Hyogo University of   Education)