Editorial: Focus on disordered, self-assembled neuromorphic systems

Kuncic, Zdenka; Nakayama, Tomonobu; Gimzewski, James K.

doi:10.1088/2634-4386/ac91a0

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…[ 5 ] Among metallic networks, [ 6,7 ] silver (Ag) NW networks have attracted great attention for the realization of neuromorphic devices and architectures, [ 1,5,8,9 ] as the electric field required to drive the dissolution/nucleation processes is lower than that required of other metals. [ 10,11 ] For this reason, silver is considered (with Cu) an electrochemically active material perfect for memristive devices, as it is prone to form

Ag+

ions which start to migrate across the memristive cell. The memristive behavior in memristive cells with an electrochemically active metal electrode is connected to the electrochemical metallization mechanism (ECM) of switching.…”

Section: Introductionmentioning

confidence: 99%

Mean Field Theory of Self‐Organizing Memristive Connectomes

et al. 2023

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Biological neuronal networks are characterized by nonlinear interactions and complex connectivity. Given the growing impetus to build neuromorphic computers, understanding physical devices that exhibit structures and functionalities similar to biological neural networks is an important step toward this goal. Self‐organizing circuits of nanodevices are at the forefront of the research in neuromorphic computing, as their behavior mimics synaptic plasticity features of biological neuronal circuits. However, an effective theory to describe their behavior is lacking. This study provides for the first time an effective mean field theory for the emergent voltage‐induced polymorphism of circuits of a nanowire connectome, showing that the behavior of these circuits can be explained by a low‐dimensional dynamical equation. The equation can be derived from the microscopic dynamics of a single memristive junction in analytical form. The effective model is tested on experiments of nanowire networks and show that it fits both the potentiation and depression of these synapse‐mimicking circuits. It is shown that this theory applies beyond the case of nanowire networks by formulating a general mean‐field theory of conductance transitions in self‐organizing memristive connectomes.

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Section: Introductionmentioning

confidence: 99%

Mean Field Theory of Self‐Organizing Memristive Connectomes

et al. 2023

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“…A particularly successful neuromorphic computing approach is the implementation of spike-based neural network algorithms in CMOS-based neuromorphic hardware 2 , 12 – 17 . An alternate neuromorphic computing approach is to exploit brain-like physical properties exhibited by novel nano-scale materials and structures 18 – 22 , including, in particular, the synapse-like dynamics of resistive memory (memristive) switching 4 , 23 – 31 .…”

Section: Introductionmentioning

confidence: 99%

Online dynamical learning and sequence memory with neuromorphic nanowire networks

Zhu,

Lilak,

Loeffler

et al. 2023

Nat Commun

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Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.

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“…Most of existing neuromorphic devices cannot be reconfigured to fulfill the diverse run-time requirements, and hence depend on tailored designs specific to targeted applications. [26,27] For example, neurons for specific activation functions, [28,29] artificial dendrites, [30] and physical reservoir computing [31] are difficult to be reconfigured thus far which play a vital role in neuromorphic computing. Furthermore, energy-and area-efficient neuromorphic hardware imposes stringent requirements for the integration of multiple sophisticated brain-like functions in an all-in-one manner.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Chen

Guo

et al. 2023

Advanced Materials

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Neuromorphic computing has been attracting ever‐increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post‐Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data‐intensive computing in that domain. Reconfigurable neuromorphic computing, an on‐demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain‐inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, we comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration‐level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.This article is protected by copyright. All rights reserved

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Editorial: Focus on disordered, self-assembled neuromorphic systems

Cited by 4 publications

References 6 publications

Mean Field Theory of Self‐Organizing Memristive Connectomes

Mean Field Theory of Self‐Organizing Memristive Connectomes

Online dynamical learning and sequence memory with neuromorphic nanowire networks

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

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