A biologically constrained learning mechanism in networks of formal neurons
type: Item
instance of: scholarly article
title: A biologically constrained learning mechanism in networks of formal neurons
zbMATH Open document ID: 0628.92028
DOI: 10.1007/BF01020645
author: L. Personnaz
author: G. Toulouse
author: Isabelle Guyon
author: Gérard Dreyfus
published in: Journal of Statistical Physics
publication date: 1986-01-01T00:00:00Z
review text: A new learning mechanism is proposed for networks of formal neurons analogous to Ising spin systems; it brings such models substantially closer to biological data in three respects: first, the learning procedure is applied initially to a network with random connections (which may be similar to a spin-glass system), instead of starting from a system void of any knowledge (as in the Hopfield model); second, the resultant couplings are not symmetrical; third, patterns can be stored without changing the sign of the coupling coefficients. It is shown that the storage capacity of such networks is similar to that of the Hopfield network, and that it is not significantly affected by the restriction of keeping the couplings' signs constant throughout the learning phase. Although this approach does not claim to model the central nervous system, it provides new insight on a frontier area between statistical physics, artificial intelligence, and neurobiology.
Mathematics Subject Classification ID: 92F05
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 82B99
zbMATH DE Number: 4023104
zbMATH Keywords: associative memory
zbMATH Keywords: biological memory
zbMATH Keywords: networks of formal neurons
zbMATH Keywords: Ising spin systems
zbMATH Keywords: learning procedure
zbMATH Keywords: random connections
zbMATH Keywords: spin-glass system
zbMATH Keywords: storage capacity
zbMATH Keywords: Hopfield network
zbMATH Keywords: neurobiology
MaRDI profile type: Publication
Wikidata QID: Q114961107
cites work: The existence of persistent states in the brain
cites work: Neural networks and physical systems with emergent collective computational abilities.
cites work: Collective properties of neural networks: A statistical physics approach
full work available at URL: https://doi.org/10.1007/bf01020645
OpenAlex ID: W1970088223
description: scientific article; zbMATH DE number 4023104