Form of artificial neural network
A Hopfield network (associative memory or Ising–Lenz–Little model or Nakano-Amari-Hopfield network ) is a spin glass system used to model neural networks , based on Ernst Ising 's work with Wilhelm Lenz on the Ising model of magnetic materials.[ 1] Hopfield networks were first described with respect to recurrent neural networks independently by Kaoru Nakano in 1971 [ 2] [ 3] and Shun'ichi Amari in 1972,[ 4] [ 5] and with respect to biological neural networks by William Little in 1974,[ 6] and were popularised by John Hopfield in 1982.[ 7]
Hopfield networks serve as content-addressable ("associative") memory systems with binary threshold nodes , or with continuous variables.[ 8] Hopfield networks also provide a model for understanding human memory.[ 9] [ 10]
^ Brush, Stephen G. (1967). "History of the Lenz-Ising Model". Reviews of Modern Physics . 39 (4): 883–893. Bibcode :1967RvMP...39..883B . doi :10.1103/RevModPhys.39.883 .
^ Nakano, Kaoru (1971). "Learning Process in a Model of Associative Memory". Pattern Recognition and Machine Learning . pp. 172–186. doi :10.1007/978-1-4615-7566-5_15 . ISBN 978-1-4615-7568-9 .
^ Nakano, Kaoru (1972). "Associatron-A Model of Associative Memory". IEEE Transactions on Systems, Man, and Cybernetics . SMC-2 (3): 380–388. doi :10.1109/TSMC.1972.4309133 .
^ Amari, Shun-Ichi (1972). "Learning patterns and pattern sequences by self-organizing nets of threshold elements". IEEE Transactions . C (21): 1197–1206.
^ Schmidhuber, Juergen (2022). "Annotated History of Modern AI and Deep Learning". arXiv :2212.11279 [cs.NE ].
^ Little, W. A. (1974). "The Existence of Persistent States in the Brain". Mathematical Biosciences . 19 (1–2): 101–120. doi :10.1016/0025-5564(74)90031-5 .
^ Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities" . Proceedings of the National Academy of Sciences . 79 (8): 2554–2558. Bibcode :1982PNAS...79.2554H . doi :10.1073/pnas.79.8.2554 . PMC 346238 . PMID 6953413 .
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^ Amit, D.J. (1992). Modeling Brain Function: The World of Attractor Neural Networks . Cambridge University Press. ISBN 978-0-521-42124-9 .
^ Rolls, Edmund T. (2016). Cerebral Cortex: Principles of Operation . Oxford University Press. ISBN 978-0-19-878485-2 .