Abstract:Learning with physical systems is an emerging paradigm that seeks to harness the intrinsic nonlinear dynamics of physical substrates for learning. The impetus for a paradigm shift in how hardware is used for computational intelligence stems largely from the unsustainability of artificial neural network software implemented on conventional transistor-based hardware. This Perspective highlights one promising approach using physical networks comprised of resistive memory nanoscale components with dynamically reconfigurable, self-organising electrical circuitry. Experimental advances have revealed the non-trivial interactions within these Self-Organising Memristive Networks (SOMNs), offering insights into their collective nonlinear and adaptive dynamics, and how these properties can be harnessed for learning using different hardware implementations. Theoretical approaches, including mean-field theory, graph theory, and concepts from disordered systems, reveal deeper insights into the dynamics of SOMNs, especially during transitions between different conductance states where criticality and other dynamical phase transitions emerge in both experiments and models. Furthermore, parallels between adaptive dynamics in SOMNs and plasticity in biological neuronal networks suggest the potential for realising energy-efficient, brain-like continual learning. SOMNs thus offer a promising route toward embedded edge intelligence, unlocking real-time decision-making for autonomous systems, dynamic sensing, and personalised healthcare, by embedding continuous learning in resource-constrained environments. The overarching aim of this Perspective is to show how the convergence of nanotechnology, statistical physics, complex systems, and self-organising principles offers a unique opportunity to advance a new generation of physical intelligence technologies.
| Comments: | Perspective paper on SOMN, to appear in NatRevPhys; 24 pages, double columns, 7 figures, 2 boxes; |
| Subjects: | Disordered Systems and Neural Networks (cond-mat.dis-nn); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Soft Condensed Matter (cond-mat.soft); Emerging Technologies (cs.ET); Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.00747 [cond-mat.dis-nn] |
| (or arXiv:2509.00747v2 [cond-mat.dis-nn] for this version) | |
| https://doi.org/10.48550/arXiv.2509.00747 arXiv-issued DOI via DataCite |
From: Francesco Caravelli [view email]
[v1]
Sun, 31 Aug 2025 08:44:02 UTC (6,263 KB)
[v2]
Sat, 25 Apr 2026 12:36:47 UTC (7,571 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。