Associatron-A Model of Associative Memory
Abstract
Thinking in the human brain greatly depends upon association mechanisms which can be utilized in machine intelligence. An associative memory device, called " Associatron," is proposed. The Associatron stores entities represented by bit patterns in a distributed manner and recalls the whole of any entity from a part of it. If the part is large, the recalled entity will be accurate; on the other hand, if the part is small, the recalled entity will be rather ambiguous. Any number of entities can be stored, but the accuracy of the recalled entity decreases as the number of entities increases. The Associatron is considered to be a simplified model of the neural network and can be constructed as a cellular structure, where each cell is connected to only its neighbor cells and all cells run in parallel. From its mechanisms some properties are derived that are expected to be utilized for human-like information processing. After these properties have been analyzed, an Associatron which deals with entities composed of less than 180 bits is simulated by a computer. Simple examples of its applications for concept formation and game playing are presented and the thinking process by the sequence of associations is described.
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The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280 · see citing works
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Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450 · see citing works
xLSTM: Extended Long Short-Term Memory
Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-3417 · Cited by 286 · see citing works
xLSTM: Extended Long Short-Term Memory
Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2024) · arXiv · doi:10.48550/arxiv.2405.04517 · Cited by 299 · see citing works
- Authors
- Nakano, K.
- Affiliations
- The University of Tokyo (JP)
- Corresponding author
- Nakano, Kaoru
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics
- Published
- 1972-07
- Volume
- SMC-2
- Issue
- 3
- Pages
- 380-388
- ISSN
- 0018-9472 (ISSN-L)
- 2168-2909
- OpenAlex
- W2075380850
- Cited by
- 369
- References
- 1
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 5 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 2 · 2019: 3 · 2018: 8 · 2017: 2 · 2016: 3 · 2015: 3 · 2014: 5 · 2013: 5 · 2012: 7
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- associative memory, distributed memory, neural network model, cellular neural networks, parallel computing, concept formation, game playing, machine intelligence
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