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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
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
Academic field: Physics
Specialties:
  • Biophysics
  • Statistical and Soft Matter Physics
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:
In refereeing

Reports on this Submission

Report #1 by Anonymous (Referee 1) on 2025-4-1 (Invited Report)

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.

Report

Please read the attached report.

Requested changes

Please read the attached report.

Attachment


Recommendation

Ask for minor revision

  • validity: good
  • significance: good
  • originality: good
  • clarity: good
  • formatting: good
  • grammar: good

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