Avalanches and the edge-of-chaos in neuromorphic nanowire networks

The brain's efficient information processing is enabled by the interplay between it's neuro-synaptic elements and complex network structure. This work reports on the dynamics of nanowire networks (NWNs), a unique neuromorphic system with synapse-like memristive junctions embedded within a neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, suggesting that dynamics are consistent with a critical-like state. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos, as measured by the maximal Lyapunov exponent. When networks are tested on increasingly complex learning tasks, dynamical states near the edge-of-chaos are found to optimise information processing. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, with potential to be applied as a physical, brain-inspired information processor.