Abstract
A new conceptual framework and a minimization principle together provide an understanding of computation in model neural circuits. The circuits consist of nonlinear graded-response model neurons organized into networks with effectively symmetric synaptic connections. The neurons represent an approximation to biological neurons in which a simplified set of important computational properties is retained. Complex circuits solving problems similar to those essential in biology can be analyzed and understood without the need to follow the circuit dynamics in detail. Implementation of the model with electronic devices will provide a class of electronic circuits of novel form and function.
Keywords
Electronic circuitComputer scienceBiological neural networkComputationNonlinear systemArtificial neural networkClass (philosophy)Set (abstract data type)Function (biology)Topology (electrical circuits)Theoretical computer scienceAlgorithmArtificial intelligenceMathematicsPhysicsMachine learningBiologyElectrical engineeringEngineering
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Publication Info
- Year
- 1986
- Type
- article
- Volume
- 233
- Issue
- 4764
- Pages
- 625-633
- Citations
- 2107
- Access
- Closed
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Cite This
J. J. Hopfield,
David W. Tank
(1986).
Computing with Neural Circuits: A Model.
Science
, 233
(4764)
, 625-633.
https://doi.org/10.1126/science.3755256
Identifiers
- DOI
- 10.1126/science.3755256