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Understanding biological computation: reliable learning and recognition.

By T Hogg and B A Huberman

Abstract

We experimentally examine the consequences of the hypothesis that the brain operates reliably, even though individual components may intermittently fail, by computing with dynamical attractors. Specifically, such a mechanism exploits dynamic collective behavior of a system with attractive fixed points in its phase space. In contrast to the usual methods of reliable computation involving a large number of redundant elements, this technique of self-repair only requires collective computation with a few units, and it is amenable to quantitative investigation. Experiments on parallel computing arrays show that this mechanism leads naturally to rapid self-repair, adaptation to the environment, recognition and discrimination of fuzzy inputs, and conditional learning, properties that are commonly associated with biological computation

Topics: Research Article
Year: 1984
DOI identifier: 10.1073/pnas.81.21.6871
OAI identifier: oai:pubmedcentral.nih.gov:392034
Provided by: PubMed Central
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