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Connection between Hebbian Unlearning and Steady States Generated by Nonequilibrium Dynamics

Agnish Kumar Behera1, Matthew Du1,2, Uday K. Jagadisan3, Srikanth Sastry4, Madan Rao5, and Suriyanarayanan Vaikuntanathan1,2

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PRX Life 3, 043010 – Published 12 November, 2025

DOI: https://doi.org/10.1103/vkt6-bltp

Abstract

The classic paradigms for learning and memory recall focus on the strengths of synaptic couplings and how these can be modulated to encode memories. In this work, using analytical theory and computational inference schemes that use specialized restricted Boltzmann machines, we show that the dynamical steady state accessed due to a class of nonequilibrium detailed balance breaking dynamics is in fact similar to those accessed after the operation of a classic unsupervised scheme for improving memory recall, Hebbian unlearning, or “dreaming.” Our work suggests how nonequilibrium dynamics can provide an alternative route for controlling the memory encoding and retrieval properties of a variety of synthetic (neuromorphic) and biological systems.

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PRX Life Collection on adaptation and learning

PRX Life presents a special collection on ‘Biological systems that learn’, curated by the organizers of the National Institute for Theory and Mathematics in Biology (NITMB) workshop together with early-career researchers. The papers in this collection illustrate how living systems acquire functions, showing it is useful to describe them as tunable matter with many individually adjustable degrees of freedom characterizing microscopic properties. Drawing inspiration from AI and non-biological systems that can acquire desired functions, the featured research highlights the timeliness of the topic and its potential to drive unifying frameworks for emergent collective function in living tunable matter.

Every article in this collection is subject to a rigorous peer review process, upholding the high standards applied to all papers in the journal. The PRX Life editorial team manages peer review and makes all editorial decisions.

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PRX Life 3, 043010– Published 12 November, 2025
  • Received 12 March 2025
  • Accepted 24 September 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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