A biologically constrained learning mechanism in networks of formal neurons

DOI10.1007/BF01020645zbMath0628.92028OpenAlexW1970088223WikidataQ114961107 ScholiaQ114961107MaRDI QIDQ1093596

L. Personnaz, G. Toulouse, Gérard Dreyfus, Isabelle Guyon

Publication date: 1986

Published in: Journal of Statistical Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01020645

zbMATH Keywords

Hopfield networkassociative memorystorage capacityneurobiologylearning procedureIsing spin systemsbiological memorynetworks of formal neuronsrandom connectionsspin-glass system

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Equilibrium statistical mechanics (82B99) Other natural sciences (mathematical treatment) (92F05)

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A cognitive and associative memory, Interfacial properties of a driven diffusive system., Stochastic analyses of the dynamics of generalized Little-Hopfield-Hemmen type neural networks, A network of multitate units capable of associative memory and pattern classification

Cites Work

• The existence of persistent states in the brain
• Collective properties of neural networks: A statistical physics approach
• Neural networks and physical systems with emergent collective computational abilities.