Nanoscale neuromorphic networks and criticality: a perspective

Abstract

Numerous studies suggest critical dynamics may play a role in information processing and task performance in biological systems. However, studying critical dynamics in these systems can be challenging due to many confounding biological variables that limit access to the physical processes underpinning critical dynamics. Here we offer a perspective on the use of abiotic, neuromorphic nanowire networks as a means to investigate critical dynamics in complex adaptive systems. Neuromorphic nanowire networks are composed of metallic nanowires and possess metal-insulator-metal junctions. These networks self-assemble into a highly interconnected, variable-density structure and exhibit nonlinear electrical switching properties and information processing capabilities. We highlight key dynamical characteristics observed in neuromorphic nanowire networks, including persistent fluctuations in conductivity with power law distributions, hysteresis, chaotic attractor dynamics, and avalanche criticality. We posit that neuromorphic nanowire networks can function effectively as tunable abiotic physical systems for studying critical dynamics and leveraging criticality for computation.

Self-organized nanoscale networks: are neuromorphic properties conserved in realistic device geometries?

“…Many different types of neuromorphic networks have been investigated, taking advantage of building blocks that include a variety of types of nanowires and nanoparticles [2][3][4][5][6][7][8][9][10][11]. Recent advances include demonstrations of criticality [4,12] and edge of chaos learning [13]. Furthermore, various computational tasks were successfully demonstrated using approaches [14][15][16] based on reservoir computing (RC) [17], where the non-linear dynamics and memory capacity of the network are exploited for information processing.…”

Pacemaker translocations and power laws in 2D stem cell-derived cardiomyocyte cultures

“…The doubly truncated power law exponent (α = -1.583), calculated using MLE methods, from Table 2 presents a particularly intriguing possibility: that pacemaker translocations could represent a critical system. Critical systems are systems that demonstrate scale invariant spatiotemporal dynamics, long-range correlations, self-similarity (fractal structures), and power laws, among other features [ 45 , 68 – 73 ]. These systems operate at or near a critical point between subcritical (“ordered”) and supercritical (“disordered”) configurations, analogous to the critical point separating phases of matter in a phase diagram.…”

“…word frequency, earthquake intensity and wildfire frequency) and biotic systems (e.g. animal migration patterns, neurons and the brain) [ 45 , 70 , 72 , 73 ]. Importantly, many of these critical systems demonstrated a power law exponent of α = -1.5, which differs by only ~5.5% from the α calculated in this study.…”

“…While intriguing, the applied methodologies in these studies fail to meet the standards defined in other fields, including: condensed matter physics, geology, and neuroscience, where power law analysis is more established [ 42 – 45 ]. In these fields, power laws are typically evaluated through a combination of methods, including: 1) calculation of the power law exponent, α, via maximum likelihood estimation (MLE), 2) statistical assessments of how well the data fit to a proposed distribution using the Kolmogorov-Smirnov goodness-of-fit test, 3) log-likelihood ratio tests between power law ( Eq 1 ), exponential ( Eq 2 ), and other heavy-tailed distributions (i.e.…”

Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility

“…The stateful temporal logic algebra system is realizable as a neuromorphic circuit built with the seven building blocks FA , LA , D , C , M , I , R and is implementable for various hardware target architectures. It is especially suited for implementation in CMOS (Nair et al, 2020 ; Han et al, 2021 ), FPGA (Yang et al, 2021a ), and quantum-based hardware (Varadarajan, 2014 ; Gonzalez-Raya et al, 2019 ; Hamilton et al, 2019 ; Shi et al, 2019 ; Lamata, 2020 ; Marković et al, 2020 ) as nanobridge atomic switch FPGAs (Demis et al, 2015 ; Sharma et al, 2021 ) superconducting accelerators (Tzimpragos et al, 2020 ; Vakili et al, 2020 ; Feldhoff and Toepfer, 2021 ), superconducting nanowires (Toomey et al, 2019 ), nanowire networks (Diaz-Alvarez et al, 2020 ; Kendall et al, 2020 ; Kuncic et al, 2020 ; Li et al, 2020 ; Milano et al, 2020 ; Dunham et al, 2021 ; Kendall, 2021 ) and memristors (Sanz et al, 2018 ; Woźniak et al, 2020 ).…”