Paper details

Title: Neural networks and physical systems with emergent collective computational abilities

Authors: J J Hopfield, Wulfram Gerstner , Werner M. Kistler, Richard Naud , Liam Paninski

Abstract: Obtained from CrossRef

Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

Work publication date: 1982-04-01

OpenAlex: https://openalex.org/W2128084896

CODECHECK details

Certificate identifier: 2020-003

Codechecker name: Daniel Nüst

Time of check: 2020-04-06

Repository: https://github.com/codecheckers/Hopfield-1982

Full certificate: https://doi.org/10.5281/zenodo.3741797

Certificate on Wikidata: Q141269513

Type: community

Venue: codecheck

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