Quasi-optimized memorization and retrieval dynamics in sparsely connected neural network models

Abstract
Several network topologies with sparse connectivity suitable for information processing in neural network models are studied. A trial and error scheme for quasi-optimal neighbour search is shown to successfully overcome instabilities giving rise to chaotic behaviour. Moreover, computer simulations reveal that for random unbiased patterns the connectivity of the network can be adapted to the specific structure of the information the network is asked to capture such that it can be quasi-optimally stabilized. On the other hand, networks with random or purely nearest neighbour interactions are not competitive candidates for the realization of associative memories. It is shown further that sparsely connected network models with quasi-optimal neighbour search techniques substantially outperform their fully connected counterparts.