Constraint satisfaction problems and neural networks: a statistical physics perspective

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dc.creatorMezard, Marc
dc.creatorMora, Thierry
dc.date2008-03-20
dc.date.accessioned2026-07-07T09:28:45Z
dc.date.available2026-07-07T09:28:45Z
dc.descriptionA new field of research is rapidly expanding at the crossroad between statistical physics, information theory and combinatorial optimization. In particular, the use of cutting edge statistical physics concepts and methods allow one to solve very large constraint satisfaction problems like random satisfiability, coloring, or error correction. Several aspects of these developments should be relevant for the understanding of functional complexity in neural networks. On the one hand the message passing procedures which are used in these new algorithms are based on local exchange of information, and succeed in solving some of the hardest computational problems. On the other hand some crucial inference problems in neurobiology, like those generated in multi-electrode recordings, naturally translate into hard constraint satisfaction problems. This paper gives a non-technical introduction to this field, emphasizing the main ideas at work in message passing strategies and their possible relevance to neural networks modeling. It also introduces a new message passing algorithm for inferring interactions between variables from correlation data, which could be useful in the analysis of multi-electrode recording data.
dc.descriptionPrepared for the proceedings of the 2007 Tauc Conference on Complexity in Neural Network Dynamics
dc.identifierhttps://arxiv.org/abs/0803.3061
dc.identifierhttp://arxiv.org/abs/0803.3061
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/157539
dc.subjectNeurons and Cognition
dc.subjectDisordered Systems and Neural Networks
dc.titleConstraint satisfaction problems and neural networks: a statistical physics perspective
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