Reservoir Computing with Neuromemristive Nanowire Networks

Abstract

As supervisor for the candidature upon which this thesis is based, I can confirm that the authorship attribution statements above are correct.

Neuromorphic nanowire networks: principles, progress and future prospects for neuro-inspired information processing

“…Their results thus demonstrate proof of concept of a neuromorphic NWN device for supervised learning using associative memory. Additionally, simulations have demonstrated MNIST handwritten digit recognition and forecasting the chaotic Mackey-Glass time series using a reservoir computing implementation, in which the input signals are nonlinearly mapped into the high-dimensional non-Markovian NWN and training are performed only on the memoryless readouts, which is linearly separable [76,100].…”

Nanoscale neuromorphic networks and criticality: a perspective

“…Simulation-based studies also lend support to the experimental data, demonstrating several information processing and learning tasks implemented in the RC framework, including chaotic time series prediction, memory capacity, transfer learning, and multitask learning [99][100][101][102][103][104]. Indeed, one study reported that neuromorphic nanowire networks prepared in an edge-of-chaos state (i.e.…”

“…This method is advantageous in that it only requires weighting and manipulation of the output layer, greatly reducing the training cost and improving power efficiency in contrast to conventional computing architectures. Consequently, neuromorphic networks have been extensively explored as a suitable substrate for RC and have successfully realized high fidelity, low power implementation of both simple and complex tasks, including logic tasks, T-maze tests, speech recognition and associative memory among others [95][96][97][98][99].…”

Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks

“…Ongoing efforts to develop memristive hardware for neuromorphic computing include not only ASNs, but also patterned crossbar arrays, and nanoparticle clusters (Moon et al, 2019;Du et al, 2017;Alibart et al, 2013;Sattar et al, 2013;Tappertzhofen et al, 2012). ASN-based devices provide a physical system with structure and functional dynamics reminiscent of the mammalian brain (Srinivasa and Cruz-Albrecht, 2012;Avizienis et al, 2012;Türel et al, 2004;Calimera et al, 2013) that has previously been employed as a computational material for applications in Reservoir Computing (RC) (Lukoševičius and Jaeger, 2009;Schrauwen et al, 2007;Snyder et al, 2012;Du et al, 2017;Goudarzi et al, 2014;Sillin et al, 2013;Fu et al, 2020). The atomic switch is a nanoscale electroionic element consisting of a Metal-Insulator-Metal (MIM) junction whose properties can be manipulated via a time-dependent input signal (Zhu et al, 2020;Kuncic et al, 2020;Manning and et al, 2018;Manning et al, 2017).…”

“…As an alternative to simulation-driven RC, in-materio RC leverages material complexity for computational purposes (Teuscher, 2017;Konkoli et al, 2018;Tanaka et al, 2019;Nakajima, 2020). Whereas early implementations of RC simply utilized a body of a liquid acting as the dynamic reservoir, more recent works harnessed the intrinsic properties of complex physical systems, including ASNs, as the basis for a computation (Lukosevicius, 2011;Lukoševičius et al, 2012;Snyder et al, 2012;Goudarzi et al, 2014;Fu et al, 2020). Software RC has historically been demonstrated as a suitable method for a litany of complex tasks including pattern classification, signal generation and temporal based logic tasks (Tanaka et al, 2019).…”