Neuromorphic Information Processing with Nanowire Networks

Zdenka Kuncic, Omid Kavehei, Ruomin Zhu, Alon Loeffler, Kaiwei Fu, Joel Hochstetter, Mike Li, James M. Shine, Adrian Diaz-Alvarez, Adam Z. Stieg, James K. Gimzewski, Tomonobu Nakayama

Abstract: Biological neural networks, unlike artificial neural networks (ANNs), can process information from data that is inherently noisy, unstructured, sparse and dynamic. How is this possible? And how can we replicate this? A crucial clue comes from neuroscience: the brain is a complex physical system and the network topology of its neural circuitry is a determinant of its emergent collective properties. Indeed, as the brain's neural network is already entrained in its physical hardware, it clearly does not require an ANN software add-on to learn from natural data. Self-assembled nanowire networks with memristive junctions represent arguably the closest hardware architecture to real biological neural networks and are thus uniquely placed to demonstrate genuinely neuromorphic information processing. Here, we present preliminary results on polymer-coated silver nanowire networks. Their neuromorphic architecture (densely structured network topology, memristive switch junctions and efficient interconnect) gives rise to a rich repertoire of collective nonlinear dynamics manifested through adaptive current transport pathways. The potential for associative learning is demonstrated in a test protocol in which a nanowire network is stimulated by multiple electrodes mapped to different spatial patterns. The capacity to process information in the temporal domain is demonstrated via simulations of a reservoir computing implementation in which nanowire networks are shown to perform tasks such as time series prediction and handwritten digit recognition. Overall, their unique properties and neuromorphic information processing capabilities make nanowire networks promising candidates for emerging applications in cognitive devices in particular, at the edge.