An n-level field theory of biological neural networks

DOI10.1007/BF00168045zbMath0788.92003OpenAlexW2072420382WikidataQ48379150 ScholiaQ48379150MaRDI QIDQ688480

Gilbert A. Chauvet

Publication date: 21 November 1993

Published in: Journal of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00168045

zbMATH Keywords

non-symmetryhierarchical network\(n\)-level field theorycontinuous neural networkdiscrete neural networkfield operatorfunctional interactionnew learning rulesnon- uniformity

Mathematics Subject Classification ID

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

Related Items (4)

S-PROPAGATORS: A FORMALISM FOR THE HIERARCHICAL ORGANIZATION OF PHYSIOLOGICAL SYSTEMS. APPLICATION TO THE NERVOUS AND THE RESPIRATORY SYSTEMS ⋮ A biologically based model of functional properties of the hippocampus ⋮ On associative motor learning by the cerebellar cortex: From Purkinje unit to network with variational learning rules ⋮ On the role of anatomy in learning by the cerebellar cortex

Uses Software

• ART 3

Cites Work

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• Adaptive filter model of the cerebellum
• A neuron field theory: Mathematical approaches to the problem of large numbers of interacting nerve cells
• Stabilization of Hebbian neural nets by inhibitory learning
• Habituation rules for a theory of the cerebellar cortex
• Existence and stability of local excitations in homogeneous neural fields
• Non-locality in biological systems results from hierarchy
• Collective properties of neural networks: A statistical physics approach
• Cellular neural networks: theory
• Modeling Brain Function
• A field theory of neural nets: I: Derivation of field equations
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Nonlinear difference-differential equations in prediction and learning theory.
• Correlation Matrix Memories

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