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 enabling embedded 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.

Memristive reservoirs draw inspiration from a novel class of neuromorphic The synaptic sites of nanowire networks are not directly accessible, hardware known as nanowire networks. These systems in contrast to random access memories (RAM) [4, 9, 17], where display emergent brain-like dynamics, with optimal performance each memory cell is addressable and programmable. The lack of controllability demonstrated at dynamical phase transitions. In these networks, is compensated for by the dynamic nature of nanowire a limited number of electrodes are available to modulate system networks, which is a key feature that enables them to adapt to dynamics, in contrast to the global controllability offered by neuromorphic evolving input signals. Nevertheless, it is worth investigating how hardware through random access memories. We demonstrate these neuromorphic systems can be optimized for information processing that the learn-to-learn framework can effectively address this tasks. For example, previous studies have shown that in a challenge in the context of optimization. Using the framework, we physical reservoir computing framework, nanowire networks can successfully identify the optimal hyperparameters for the reservoir.

Some neuroscience theories suggest the human brain operates best'at the edge of chaos'. Now scientists have found that keeping a nanowire network at the edge of becoming chaotic is the best state for it to produce useful signals to solve problems. Scientists at the University of Sydney and Japan's National Institute for Material Science (NIMS) have discovered that an artificial network of nanowires can be tuned to respond in a brain-like way when electrically stimulated. The international team, led by Joel Hochstetter with Professor Zdenka Kuncic and Professor Tomonobu Nakayama, found that by keeping the network of nanowires in a brain-like state "at the edge of chaos," it performed tasks at an optimal level. This, they say, suggests the underlying nature of neural intelligence is physical, and their discovery opens an exciting avenue for the development of artificial intelligence.

Researchers have demonstrated how to keep a network of nanowires in a state that's right on what's known as the edge of chaos – an achievement that could be used to produce artificial intelligence (AI) that acts much like the human brain does. The team used varying levels of electricity on a nanowire simulation, finding a balance when the electric signal was too low when the signal was too high. If the signal was too low, the network's outputs weren't complex enough to be useful; if the signal was too high, the outputs were a mess and also useless. "We found that if you push the signal too slowly the network just does the same thing over and over without learning and developing. If we pushed it too hard and fast, the network becomes erratic and unpredictable," says physicist Joel Hochstetter from the University of Sydney and the study's lead author.

Australian and Japanese scientists have discovered that an artificial network of nanowires may physically function at its peak at the'edge of chaos', much like the human brain. The team, led by Joel Hochstetter of the University of Sydney, ran computer simulations to test how a random nanowire network, a type of artificial intelligence, best performs tasks. They found that the wires acted almost like neurons. The information processing in the nanowire network was physical and required minimal direction once stimulated, much like the brain, but problem-solved better with the right level of stimulation. When the signal stimulating the network was too low, there was too much order and predictability for it to produce complex outputs, but when there was too much stimulation, the output was chaotic and useless for problem solving.

IMAGE: An artist's impression of a neural network (left) next to an optical micrograph of a physical nanowire network. Scientists at the University of Sydney and Japan's National Institute for Material Science (NIMS) have discovered that an artificial network of nanowires can be tuned to respond in a brain-like way when electrically stimulated. The international team, led by Joel Hochstetter with Professor Zdenka Kuncic and Professor Tomonobu Nakayama, found that by keeping the network of nanowires in a brain-like state "at the edge of chaos", it performed tasks at an optimal level. This, they say, suggests the underlying nature of neural intelligence is physical, and their discovery opens an exciting avenue for the development of artificial intelligence. The study is published today in Nature Communications.

Scientists at the University of Sydney and Japan's National Institute for Material Science (NIMS) have discovered that an artificial network of nanowires can be tuned to respond in a brain-like way when electrically stimulated. The international team, led by Joel Hochstetter with Professor Zdenka Kuncic and Professor Tomonobu Nakayama, found that by keeping the network of nanowires in a brain-like state "at the edge of chaos", it performed tasks at an optimal level. This, they say, suggests the underlying nature of neural intelligence is physical, and their discovery opens an exciting avenue for the development of artificial intelligence. The study is published today in Nature Communications. "We used wires 10 micrometers long and no thicker than 500 nanometres arranged randomly on a two-dimensional plane," said lead author Joel Hochstetter, a doctoral candidate in the University of Sydney Nano Institute and School of Physics.