Neuromorphic behaviour in discontinuous metal films

Bose, Supratik; Mallinson, Josh; Galli, Edoardo; Acharya, Susant Kumar; Minnai, Chloé; Bones, Philip J.; Brown, S. A.

doi:10.1039/d1nh00620g

Cited by 6 publications

(2 citation statements)

References 60 publications

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“…Similar approaches of implementation of RC here discussed in nanowire networks can be extended to other complex nanostructured systems such as nanoparticle networks [58,59], discontinuous metallic films [60], nanotubes [61], polymeric substrates [43] and electrochemical systems [62]. While experimental hardware realization and characterization of multiterminal nanonetwork reservoirs with a large number of electrodes and a fine control of temporal dynamics still represent a challenge, it is important to point out that the here reported grid-graph model allows to explore different RC implementations and architectures in silico with low computational sources for a rational design of neuromorphic hardware.…”

Section: Discussionmentioning

confidence: 96%

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

Milano

Montano

Ricciardi

2023

J. Phys. D: Appl. Phys.

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Physical reservoir computing represents a computational framework that exploits information-processing capabilities of programmable matter, allowing the realization of energy-efficient neuromorphic hardware with fast learning and low training cost. Despite self-organized memristive networks have been demonstrated as physical reservoir able to extract relevant features from spatiotemporal input signals, multiterminal nanonetworks open the possibility for novel strategies of computing implementation. In this work, we report on implementation strategies of in materia reservoir computing with self-assembled memristive networks. Besides showing the spatiotemporal information processing capabilities of self-organized nanowire networks, we show through simulations that the emergent collective dynamics allows unconventional implementations of reservoir computing where the same electrodes can be used as both reservoir inputs and outputs. By comparing different implementation strategies on a digit recognition task, simulations show that the unconventional implementation allows a reduction of the hardware complexity without limiting computing capabilities, thus providing new insights for taking full advantage of in materia computing toward a rational design of neuromorphic systems.

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Section: Discussionmentioning

confidence: 96%

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

Milano

Montano

Ricciardi

2023

J. Phys. D: Appl. Phys.

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“…Various physical reservoirs have been reported to meet these requirements. 20,21,23 Moreover, the 4-bit sequential coding for handwritten digits classification and compression of spatial dimension for feature space extraction through volatile memristive devices were revealed. 24,25 Milano et al demonstrated a bottom-up approach in constructing low-cost computing paradigm through self-organized nanowire networks for mapping the spatio-temporal inputs.…”

Section: Introductionmentioning

confidence: 99%

Unveiling transient current response in bilayer oxide-based physical reservoirs for time-series data analysis

Lai,

Chen,

Chaurasiya

et al. 2024

Nanoscale

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Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities

Mondal,

Attri,

Rao

et al. 2024

Applied Physics Reviews

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In the era of artificial intelligence and smart automated systems, the quest for efficient data processing has driven exploration into neuromorphic systems, aiming to replicate brain functionality and complex cognitive actions. This review assesses, based on recent literature, the challenges and progress in developing basic neuromorphic systems, focusing on “material-neuron” concepts, that integrate structural similarities, analog memory, retention, and Hebbian learning of the brain, contrasting with conventional von Neumann architecture and spiking circuits. We categorize these devices into filamentary and non-filamentary types, highlighting their ability to mimic synaptic plasticity through external stimuli manipulation. Additionally, we emphasize the importance of heterogeneous neural content to support conductance linearity, plasticity, and volatility, enabling effective processing and storage of various types of information. Our comprehensive approach categorizes fundamentally different devices under a generalized pattern dictated by the driving parameters, namely, the pulse number, amplitude, duration, interval, as well as the current compliance employed to contain the conducting pathways. We also discuss the importance of hybridization protocols in fabricating neuromorphic systems making use of existing complementary metal oxide semiconductor technologies being practiced in the silicon foundries, which perhaps ensures a smooth translation and user interfacing of these new generation devices. The review concludes by outlining insights into developing cognitive systems, current challenges, and future directions in realizing deployable neuromorphic systems in the field of artificial intelligence.

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Neuromorphic behaviour in discontinuous metal films

Abstract: Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and...

Cited by 6 publications

References 60 publications

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

Unveiling transient current response in bilayer oxide-based physical reservoirs for time-series data analysis

Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities

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