Emergent dynamics of neuromorphic nanowire networks

Abstract

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Topological Properties of Neuromorphic Nanowire Networks

“…We previously introduced a novel neuromorphic system comprised of self-assembled nanowires whose structure and function (in response to electrical stimulation) mimic that of biological neural networks (Kuncic et al, 2018;Diaz-Alvarez et al, 2019). In these networks, each junction between nanowires provides a non-linear synaptic function in a similar manner as an atomic switch (Terabe et al, 2005;Ohno et al, 2011).…”

“…In these networks, each junction between nanowires provides a non-linear synaptic function in a similar manner as an atomic switch (Terabe et al, 2005;Ohno et al, 2011). Rather than focusing on the controllability of individual synapses like ANNs or other neuromorphic systems, our Atomic Switchlike Networks (ASNs) mimic the complex topology of biological neural networks, by mimicking biological self-assembly to form similarly complex networks comprised of nanowires (synthetic neurons) and junctions (synthetic synapses) (Stieg et al, 2012;Diaz-Alvarez et al, 2019).…”

“…It is also extremely difficult to use imaging-based techniques such as or electron microscopy (e.g., White et al, 1986;Eberle and Zeidler, 2018) reconstructions to unpack the structural connectivity of ASNs, as it is impossible to tell whether or not intersecting wires form a junction between them. We therefore have developed a computational model that simulates the structure experimental ASNs, based on functional, experimental validation (Kuncic et al, 2018;Diaz-Alvarez et al, 2019). For the purposes of the present study, we use this model solely to construct simulated selfassembled networks for structural analysis.…”

“…To explore the topology of ASNs, we generated multiple networks with different structural parameters (see Figure 1 for visualizations). Hardware ASNs acquire a complex network structure through bottom-up self-assembly (Avizienis et al, 2012;Stieg et al, 2012;Diaz-Alvarez et al, 2019), similar to neural network growth in the brain. To simulate this self-assembly, we modeled nanowires as 1D objects of length uniformly drawn from a normal distribution of specified average wire length (mean of distribution, ranging from 6 to 9 µm) and wire dispersion (ratio of standard deviation to the mean, ranging from 0 to 50%).…”

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.…”