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. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing.

Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires

“…[275][276][277][278][279][280][281][282][283] In the following, resistive switching in stacked and planar devices based on NW networks is described. Resistive switching networks based on random ordered nanowires, dendritic, and fractal structures have attracted great attention because of the peculiar conduction properties that make these structures promising for resistive switching and neuromorphic applications.…”

“…[275,277,[280][281][282][283] Thus, the global resistive switching behavior of the network arises from resistive switching events located at the numerous wire interconnections. Indeed, the NW network global properties arise from junctions between individual wires that determine the network connectivity.…”

Non-ohmic behavior and resistive switching of Au cluster-assembled films beyond the percolation threshold

“…6,7 For instance, random networks of nanowires, nanoparticles, and clusters, embedded in a polymeric matrix or passivated by shell of ligands or oxide layers (insulator-conductor nanocomposites), show RS phenomena resulting in features typical of neuromorphic systems. 6,8 This has been attributed to either polymer breakdown between junctions or conducting lament formation between individual metallic nanoobjects. [9][10][11] The evolution of the electrical properties of such nanocomposites, upon the application of an electric eld, strongly depends on the volume fraction of the conductive phase and it can be described by the percolation theory.…”

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

“…Despite a bright future prospective for the development of next‐generation artificial intelligence (AI) systems, it is hard for such rigid top‐down architectures to emulate most typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Alternative ways using unconventional systems consisting of many interacting nano‐parts have been proposed for the realization of biologically plausible architectures where the emergent behavior arises from a complexity similar to that of biological neural circuits . However, these systems were unable to demonstrate bio‐realistic implementation of structural plasticity including reweighting and rewiring and spatio‐temporal processing of input signals similarly to our brain.…”

“…By applying an over‐threshold constant voltage bias, the network conductance (synaptic weight) between two pads (neuron terminals) can be gradually increased (facilitation), as shown in Figure b (details in S7, Supporting Information). The gradual enhancement of network connectivity is related to cascade switching events of memristive elements constituting the network that self‐selects the lowest‐energy path for electronic conduction . After facilitation, the synaptic weight gradually relaxes back toward the initial state, exhibiting a volatile behavior due to the spontaneous dissolution of Ag conductive filaments previously formed in memristive elements of the network .…”