Associative memory in neural networks with the Hebbian learning rule

Journal Article · Mon May 01 00:00:00 EDT 1989 · Modern Physics Letters B; (USA)

DOI:https://doi.org/10.1142/S021798498900087X· OSTI ID:5587429

Tsodyks, M V ^[1]

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1. The Institute of Higher Nervous Activity and Neurophysiology, Acad. of Sci., Moscow (SU)

The authors consider the Hopfield model with the most simple form of the Hebbian learning rule, when only simultaneous activity of pre- and post-synaptic neurons leads to modification of synapse. An extra inhibition proportional to full network activity is needed. Both symmetric nondiluted and asymmetric diluted networks are considered. The model performs well at extremely low level of activity rho < {Kappa}/sup -1/2/, where {Kappa} is the mean number of synapses per neuron.

• OSTI ID: 5587429
• Journal Information: Modern Physics Letters B; (USA), Vol. 3:7; ISSN 0217-9849
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
59 BASIC BIOLOGICAL SCIENCES
LEARNING
ALGORITHMS
COMPUTER NETWORKS
MATHEMATICAL MODELS
NERVE CELLS
ANIMAL CELLS
MATHEMATICAL LOGIC
SOMATIC CELLS
990210* - Supercomputers- (1987-1989)
990230 - Mathematics &amp; Mathematical Models- (1987-1989)
550100 - Behavioral Biology