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Critical Dynamics and Cyclic Memory Retrieval in Non-reciprocal Hopfield Networks
by Shuyue Xue, Mohammad Maghrebi, George I. Mias, and Carlo Piermarocchi
Submission summary
Authors (as registered SciPost users): | George Mias · Carlo Piermarocchi |
Submission information | |
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Preprint Link: | scipost_202501_00032v1 (pdf) |
Code repository: | https://github.com/shuyue13/non-reciprocal-Hopfield |
Date submitted: | 2025-01-16 20:34 |
Submitted by: | Piermarocchi, Carlo |
Submitted to: | SciPost Physics |
Ontological classification | |
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Academic field: | Physics |
Specialties: |
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Approaches: | Theoretical, Computational |
Abstract
We study Hopfield networks with non-reciprocal coupling inducing switches between memory patterns. Dynamical phase transitions occur between phases of no memory retrieval, retrieval of multiple point-attractors, and limit-cycles. The limit cycle phase is bounded by a Hopf bifurcation line and a fold bifurcation line. Autocorrelation scales as $\tilde{C}(\tau/N^\zeta)$, with $\zeta = 1/2$ on the Hopf line and $\zeta = 1/3$ on the fold line. Perturbations of strength $F$ on the Hopf line exhibit response times scaling as $|F|^{-2/3}$, while they induce switches in a controlled way within times scaling as $|F|^{-1/2}$ in the fold line. A Master Equation approach numerically verifies the critical behavior predicted analytically. We discuss how these networks could model biological processes near a critical threshold of cyclic instability evolving through multi-step transitions.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
Reports on this Submission
Strengths
-it addresses Hopfield nets (with just a couple of patterns) via dynamical systems rather than statistical mechanical techniques, acting as a bridge among two well established disciplines.
-it paints a clear and coherent scenario for the network under study, in particular its dynamics is investigated in great detail.
-it constistutes a simple and transparent example of the rich behavior hidden in these Hebbian networks
-the language used to write the paper is a welcome tradeoff between intuitive explanations and mathematical formality
Weaknesses
-it focuses solely on two stored patterns.
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