Memristive nanowires exhibit small-world connectivity

Pantone, Ross D.; Kendall, Jack D.; Nino, Juan C.

doi:10.1016/j.neunet.2018.07.002

Cited by 18 publications

(13 citation statements)

References 13 publications

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“…Loeffler et al [65] showed that self-assembled NWNs exhibit topological properties such as small-world propensity and modularity that are remarkably similar to biological neural networks and different from random and grid-like networks ( Figure 5). Small world connectivity in NWNs has also been inferred in a study by Pantone et al [66]. Unlike the fully connected bipartite networks used in feed-forward artificial neural networks, smallworld networks are relatively sparse, characterised by local connectivity and short path lengths.…”

Section: Network Dynamicsmentioning

confidence: 86%

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

Kuncic

Nakayama

2021

Advances in Physics: X

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Nanowire networks represent a unique class of neuromorphic systems. Their self-assembly confers a complex structure to their network circuitry, embedding a higher interconnectivity of resistive switching memory (memristive) cross-point junctions than can be achieved with top-down nanofabrication methods. Coupling of the nonlinear memristive dynamics to the network topology enables intrinsic adaptiveness and gives rise to emergent non-local dynamics. In this article, we summarise the physical principles underlying the memristive junctions and network dynamics of neuromorphic nanowire networks and provide the first comprehensive review of studies to date. We conclude with a perspective on future prospects for neuromorphic information processing.

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Section: Network Dynamicsmentioning

confidence: 86%

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

Kuncic

Nakayama

2021

Advances in Physics: X

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“…Top-down and bottom-up approaches are habitually combined to create the electrical connections necessary to characterize the structures (Kronholz et al, 2006). In the context of bio-inspired computing, we would like to highlight here the work done on nanowire networks (Stieg et al, 2012;Asayesh-Ardakani et al, 2013;Pantone et al, 2018;Hochstetter et al, 2021;Loeffler et al, 2020;Mallinson et al, 2019;Pike et al, 2020). The structure of such networks, and in particular their dynamic properties, reflect basal functionalities as observed in nervous systems, such as small-word connectivity and self-organized criticality (SOC) (Watts and Strogatz, 1998;Beggs and Plenz, 2003).…”

Section: Advanced Computing Architectures and Novel Electronic Devicesmentioning

confidence: 99%

“…One universal property of the memristive device concept is that the memristive state depends on previously induced charge flows, applied currents, or applied electric fields, thus storing a historically-determined resistance state. For (Ielmini and Waser, 2016;Xia and Yang, 2019;, (c) cartoon of a 3D nanowire network (Stieg et al, 2012;Pantone et al, 2018;Minnai et al, 2017;Mallinson et al, 2019;Loeffler et al, 2020;Hochstetter et al, 2021;, (d) 3D cross-sectional graph of a fluidic memristive device adapted from (Robin et al, 2021) with permission. details concerning resistive switching and the underlying physical-chemical mechanisms, we refer the reader to the references given in the figure caption (Fig.…”

Section: Novel Electronic Devicesmentioning

confidence: 99%

Matter & Mind Matter

Birkoben¹,

Kohlstedt²

2022

Preprint

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As a result of a hundred million years of evolution, living animals have adapted extremely well to their ecological niche. Such adaptation implies species-specific interactions with their immediate environment by processing sensory cues and responding with appropriate behavior. Understanding how living creatures perform pattern recognition and cognitive tasks is of particular importance for computing architectures: by studying these information pathways refined over eons of evolution, researchers may be able to streamline the process of developing more highly advanced, energy efficient autonomous systems. With the advent of novel electronic and ionic components along with a deeper understanding of information pathways in living species, a plethora of opportunities to develop completely novel information processing avenues are within reach. Here, we describe the basal information pathways in nervous systems, from the local neuron level to the entire nervous system network. The dual importance of local learning rules is addressed, from spike timing dependent plasticity at the neuron level to the interwoven morphological and dynamical mechanisms of the global network. Basal biological principles are highlighted, including phylogenies, ontogenesis, and homeostasis, with particular emphasis on network topology and dynamics. While in machine learning system training is performed on virgin networks without any a priori knowledge, the approach proposed here distinguishes itself unambiguously by employing growth mechanisms as a guideline to design novel computing architectures. Including fundamental biological information pathways that explore the spatiotemporal fundamentals of nervous systems has untapped potential for the development of entirely novel information processing systems. Finally, a benchmark for neuromorphic systems is suggested.

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“…[21][22][23][24][25][26][27] More recent work has addressed the topological structure of 2D nanowire networks using wellestablished techniques in graph and network theory. These networks have been shown to exhibit a small-world architecture similar to many biological systems, [27,28] i.e. the nodes are considered to be both highly clustered and have small path lengths between nodes.…”

Section: Introductionmentioning

confidence: 99%

“…that the wires lie in a plane. [23][24][25][26][27][28] This is clearly not the case for real-world nanowire networks in which the wires are stacked on top of one another during a deposition process. Given the importance of topological structure for information processing and a range of other network properties there is clearly a need for models which incorporate realistic stacking of the nanowires.…”

Section: Introductionmentioning

confidence: 99%

Nanowire networks: how does small-world character evolve with dimensionality?

Daniels,

Brown

2021

Preprint

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Networks of nanowires are currently under consideration for a wide range of electronic and optoelectronic applications. Nanowire devices are usually made by sequential deposition, which inevitably leads to stacking of the wires on top of one another. Here we demonstrate the effect of stacking on the topology of the resulting networks. We compare perfectly 2D networks with quasi-3D networks, and compare both nanowire networks to the corresponding Watts Strogatz networks, which are standard benchmark systems. By investigating quantities such as clustering, path length, modularity, and small world propensity we show that the connectivity of the quasi-3D networks is significantly different to that of the 2D networks, a result which may have important implications for applications of nanowire networks.

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Memristive nanowires exhibit small-world connectivity

Cited by 18 publications

References 13 publications

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

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

Matter & Mind Matter

Nanowire networks: how does small-world character evolve with dimensionality?

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