SYNCHRONIZATION AND ASSOCIATION IN A LARGE NETWORK OF COUPLED CHUAS CIRCUITS

This work presents the results of simulation of the fully connected ne tworks of Chua's circuits mutually coupled by nonlinear conductances d erived using the Hebbian learning rule. The network can be regarded as a generalization of the Hopfield neural network built up of chaotic u nits. Due to the space-time synchronization of units, the studied netw ork exhibits the ability of pattern retrieval and decorrelation of com plex input patterns.