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

TL;DR

This paper reveals that nanowire networks exhibit self-similar conductance scaling and naturally develop a dominant 'winner-takes-all' conduction path, which has significant implications for neuromorphic computing and memory systems.

Contribution

It demonstrates the emergent 'winner-takes-all' connectivity path in nanowire networks, linking it to low-energy states and neuromorphic device applications.

Findings

Conductance shows self-similar scaling in nanowire networks.

A specific junction class leads to conductance plateaus.

Emergent dominant conduction paths correspond to lowest-energy states.

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.