Learning and forgetting on asymmetric, diluted neural networks
Journal Article
·
· J. Stat. Phys.; (United States)
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
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GENERAL PHYSICS
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ISING MODEL
ARTIFICIAL INTELLIGENCE
STATISTICAL MECHANICS
ASYMMETRY
COMPUTER ARCHITECTURE
COMPUTERIZED SIMULATION
MEMORY DEVICES
PHASE DIAGRAMS
RANDOMNESS
SPIN
ANGULAR MOMENTUM
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DIAGRAMS
MATHEMATICAL MODELS
MECHANICS
PARTICLE PROPERTIES
SIMULATION
657002* - Theoretical & Mathematical Physics- Classical & Quantum Mechanics
990210 - Supercomputers- (1987-1989)