Computational modeling study demonstrates that nonequilibrium steady states match Hebbian unlearning in neural networks, suggesting physical mechanisms for memory control.
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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Behera et al. (2025) studied this question.
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