Optimising synaptic learning rules in linear associative memories

DOI10.1007/BF00206223zbMath0752.92002WikidataQ52102309 ScholiaQ52102309MaRDI QIDQ1182329

D. J. Willshaw, Peter Dayan

Publication date: 28 June 1992

Published in: Biological Cybernetics (Search for Journal in Brave)

zbMATH Keywords

learning ruleassociative matrix memorycovariance rulelinear associative memoriesmaximization of the signal-to-noise ratiosynaptic learning rules

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (13)

Optimal learning rules for familiarity detection ⋮ Learning flexible sensori-motor mappings in a complex network ⋮ Associative Memory with Dynamic Synapses ⋮ Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization ⋮ Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory ⋮ Iterative Retrieval and Block Coding in Autoassociative and Heteroassociative Memory ⋮ Efficient Associative Computation with Discrete Synapses ⋮ Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances ⋮ Effective Neuronal Learning with Ineffective Hebbian Learning Rules ⋮ Neural Associative Memory with Optimal Bayesian Learning ⋮ Memory Dynamics in Attractor Networks with Saliency Weights ⋮ Memory Capacities for Synaptic and Structural Plasticity ⋮ Inhomogeneities in Heteroassociative Memories with Linear Learning Rules

Cites Work

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• Neural networks and physical systems with emergent collective computational abilities.
• The space of interactions in neural network models
• Correlation Matrix Memories

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