Topology and Computational Performance of Attractor Neural Networks
Disordered Systems and Neural Networks
2009-11-10 v1
Abstract
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 net is the most efficient for storage and retrieval of patterns by the entire network. However, in the scale-free case retrieval errors are not distributed uniformly: 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. Implications for brain function and social dynamics are suggestive.
Keywords
Cite
@article{arxiv.cond-mat/0304021,
title = {Topology and Computational Performance of Attractor Neural Networks},
author = {Patrick N. Mcgraw and Michael Menzinger},
journal= {arXiv preprint arXiv:cond-mat/0304021},
year = {2009}
}
Comments
2 figures included. Submitted to Phys. Rev. Letters