Topology and computational performance of attractor neural networks.
To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies. The random configuration is the most efficient for storage and retrieval of patterns by the network as a whole. However, in the scale-free case retrieval errors are not distributed uniformly among the nodes. The portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. The implications of these findings for brain function and social dynamics are suggestive.
McGraw
Patrick N
PN
Department of Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 3H6. pmcgraw@chem.utoronto.ca
Menzinger
Michael
M