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

Abstract

Acting as artificial synapses, two‐terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Current memristive crossbar architectures demonstrate the implementation of neuromorphic computing paradigms, although they are unable to emulate typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Herein, self‐organizing memristive random nanowire (NW) networks with functional connectivity able to display homo‐ and heterosynaptic plasticity is reported thanks to the mutual electrochemical interaction among memristive NWs and NW junctions. In particular, it is shown that rewiring and reweighting effects observed in single NWs and single NW junctions, respectively, are responsible for structural plasticity of the network under electrical stimulation. Such biologically inspired systems allow a low‐cost realization of neural networks that can learn and adapt when subjected to multiple external stimuli, emulating the experience‐dependent synaptic plasticity that shape the connectivity and functionalities of the nervous system that can be exploited for hardware implementation of unconventional computing paradigms.

Anomalous electrical conduction and negative temperature coefficient of resistance in nanostructured gold resistive switching films

“…Atomic rearrangement present under the application of high voltage bias contributes to the rearrangement of grain boundaries responsible for the switching events. The observed mechanism is substantially different from what observed in random networks of nanowires where ionic transport is involved 14 , 15 , 17 , 18 , 55 . In our case the highly correlated re-arrangement of grain boundaries changes the local conductivity as observed in single metallic nanowires 56 .…”

“…Random networks of metallic nanowires/nanoparticles in a polymeric matrix or passivated by shell of ligands or oxide layers have gained a renewed interest for the fabrication of non-linear circuital elements such as memristors and resistive switching devices for analog computing and neuromorphic data processing 14 – 18 . These systems are in the weak-coupling regime and their electrical behavior is determined by the formation/destruction of conducting junctions between isolated nanoparticles conferring neuromorphic properties to the networks 14 – 17 , 19 – 21 .…”

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

“…This naturally produces a 2D spatial distribution of randomly oriented nanowires, interconnected by cross-point MIM junctions, with typical densities of 10 junctions/μm 2 and 0.5 nanowires/μm 2 [45,46]. Device electrodes can also be mask-deposited onto the substrate [47,48], thereby avoiding lithographic fabrication steps altogether. Metallic nanowires coated with either metal oxides or other electrolytes, such as Ag 2 S or PVP, result in NWN MIM junctions with resistive switching memory.…”

“…TiO 2 , NiO 2 ) [39] or, in the case of atomic switches, the chalcogenide electrolyte Ag 2 S, which undergoes a bias-catalysed transition to a metallic phase with a remarkably high diffusion coefficient for silver [45,54]. Studies have also found memristive switching in Ag-PVP nanowire systems [47,55]. This may be attributed to the amorphous phase of PVP, a polymer electrolyte, possessing more ion transport channels than a solid (i.e.…”

“…Experiments revealed reconfiguration dynamics demonstrating self-healing and fault-tolerance (see also simulation results by O'Callaghan et al [46]), as well as adaptive behaviour akin to synaptic plasticity ( Figure 10). In a separate study, Milano et al [47] found that a synaptic plasticity effect could also arise from 'rewiring' of individual Ag nanowires following nanowire breakage at sufficiently high (~mA) current densities. O'Callaghan et al [46] also found a capacitive breakdown effect in the low current regime (~pA), where individual junctions behave more like capacitors than memristors.…”