Self-Organising Memristive Networks as Physical Learning Systems
The study explores Self-Organising Memristive Networks (SOMNs), physical systems using nanoscale resistive memory components with reconfigurable circuitry to learn. Experiments show these networks exhibit brain-like properties such as plasticity and avalanche criticality. This suggests SOMNs can be used for energy-efficient, brain-like continual learning in embedded edge intelligence applications.