Dual-mode space-varying self-designing cellular neural networks for associative memory

A dual-mode space-varying CNN is proposed for associative memory. In the learning mode the CNN is used as a designer network which computes the weights to be used in the recall mode. Learning involves only local information, i.e., available inside each cell without extra interconnections, It allows to us exploit the analog and parallel computational power of the CNN chip, not only for information storage and retrieval, but also for the design of the CNN itself. Simulation results on the capacity obtained by the proposed learning algorithm are presented.