arXiv · cond-mat/9708215
Attractors in fully asymmetric neural networks
Abstract
The statistical properties of the length of the cycles and of the weights of the attraction basins in fully asymmetric neural networks (i.e. with completely uncorrelated synapses) are computed in the framework of the annealed approximation which we previously introduced for the study of Kauffman networks. Our results show that this model behaves essentially as a Random Map possessing a reversal symmetry. Comparison with numerical results suggests that the approximation could become exact in the infinite size limit.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
U. Bastolla, G. Parisi. 1997-08-28. Attractors in fully asymmetric neural networks. https://doi.org/10.1088/0305-4470%2F30%2F16%2F007
Cite the original work for its findings. Save a collection to share your selection of sources.