Statistical mechanics beyond the Hopfield model: solvable problems in neural network theory.

Full Abstract

We present four 'case study' examples of solvable problems in the theory of recurrent neural networks, which are relevant to our understanding of information processing in the brain, but which are also interesting from a purely statistical mechanical point of view, even at the level of simple models (which helps in stimulating interdisciplinary work). The examples concern issues in network dynamics, network connectivity, spike timing and synaptic plasticity.

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Author information

Author/s: Coolen, A C C (AC); Del Prete, V (V);

Affiliation: Department of Mathematics, King's College London, UK. tcoolen@mth.kcl.ac.uk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Review

Journal: Reviews in the neurosciences (Rev Neurosci), published in England. (Language: eng)

Reference: 2003-; vol 14 (issue 1-2) : pp 181-93

Dates: Created 2003/08/21; Completed 2003/09/17; Revised 2007/11/15;

PMID: 12929925, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Neural Networks (Computer)
Neuronal Plasticity - physiology
Nonlinear Dynamics
Statistics as Topic - methods
Time Factors

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