Studies on the Memory Capacity and Robustness of Chaotic Dynamic Neural Networks

A dynamical neural model that is strongly biologically motivated is applied to learning and retrieving binary patterns. This neural network, known as Freeman's K-sets, is trained with Hebbian rule and habituation to memorize the input patterns by associating them with an attractors formed in the state space. After the patterns are memorized noisy input is given to the network to recover the original. We compare the results of this recall for a different number of memories and compare them with performance of the Hopfield model. We show capacity of the dynamical system exceeds that of the Hopfield network and the noisy recall degrades at a slower pace as the number of the patterns is growing. Experimental results indicate that the critical load paramter, which gives approximation of the network capacity, is higher in K-model than in the Hopfield network. Significant advantage of K-model is achieved at a larger training set size, when compared to Hopfield model.