Emergence of Winner-takes-all Connectivity Paths in Random Nanowire Networks

Abstract

Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a "winner-takes-all" conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. These results point to the possibility of independently addressing memory or conductance states in complex systems and is expected to have important implications for neuromorphic devices based on reservoir computing.