skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Learning and forgetting on asymmetric, diluted neural networks

Journal Article · · J. Stat. Phys.; (United States)
DOI:https://doi.org/10.1007/BF01017556· OSTI ID:5371563

It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks.

Research Organization:
CEN-Saclay, Gif-sur-Yvette (France)
OSTI ID:
5371563
Journal Information:
J. Stat. Phys.; (United States), Vol. 49:5/6
Country of Publication:
United States
Language:
English