The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Computing with Neural Circuits: A Model
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.
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Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Self-citationA logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Self-citation“Neural” computation of decisions in optimization problems
Hopfield, J. J., & Tank, D. W. (1985) · Biological Cybernetics · doi:10.1007/bf00339943 · Cited by 6,031
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Generalizing the Hough transform to detect arbitrary shapes
Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,371
Cited by
Is the P300 component a manifestation of context updating?
Donchin, E., & Coles, M. G. H. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058027 · Cited by 3,585
Cellular neural networks: applications
Chua, L. O., & Yang, L. (1988) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.7601 · Cited by 2,375
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Neural syntax: cell assemblies, synapsembles, and readers
Buzsáki, G. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.023 · Cited by 1,289
A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory.
Lisman, J. (1989) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.86.23.9574 · Cited by 1,127
Metadata
- Authors
- Hopfield, J. J., & Tank, D. W.
- Venue
- Science
- Published
- 1986-08-08
- Volume
- 233
- Issue
- 4764
- Pages
- 625-633
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2010526455
- Cited by
- 2,116
- References
- 43
- FWCI
- 86.55
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
