Computer Science > Neural and Evolutionary Computing
arXiv:2212.01196 (cs)
[Submitted on 2 Dec 2022 (v1), last revised 14 Dec 2023 (this version, v2)]
Title:Vector Symbolic Finite State Machines in Attractor Neural Networks
View a PDF of the paper titled Vector Symbolic Finite State Machines in Attractor Neural Networks, by Madison Cotteret and 3 other authors
View PDFHTML (experimental)Abstract:Hopfield attractor networks are robust distributed models of human memory, but lack a general mechanism for effecting state-dependent attractor transitions in response to input. We propose construction rules such that an attractor network may implement an arbitrary finite state machine (FSM), where states and stimuli are represented by high-dimensional random vectors, and all state transitions are enacted by the attractor network's dynamics. Numerical simulations show the capacity of the model, in terms of the maximum size of implementable FSM, to be linear in the size of the attractor network for dense bipolar state vectors, and approximately quadratic for sparse binary state vectors. We show that the model is robust to imprecise and noisy weights, and so a prime candidate for implementation with high-density but unreliable devices. By endowing attractor networks with the ability to emulate arbitrary FSMs, we propose a plausible path by which FSMs could exist as a distributed computational primitive in biological neural networks.
| Comments: | 26 pages, 13 figures. This is the authors' final version before publication in Neural Computation |
| Subjects: | Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2212.01196 [cs.NE] |
| (orarXiv:2212.01196v2 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2212.01196 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1162/neco_a_01638 DOI(s) linking to related resources |
Submission history
From: Madison Cotteret [view email][v1] Fri, 2 Dec 2022 14:23:29 UTC (956 KB)
[v2] Thu, 14 Dec 2023 12:29:04 UTC (1,337 KB)
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View a PDF of the paper titled Vector Symbolic Finite State Machines in Attractor Neural Networks, by Madison Cotteret and 3 other authors
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