Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Gupta, Isha; Serb, Alexantrou; Khiat, Ali; Zeitler, Ralf; Vassanelli, Stefano; Prodromakis, Themis

doi:10.1038/ncomms12805

Cited by 153 publications

(131 citation statements)

References 45 publications

(55 reference statements)

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“…Indeed, it has even been suggested that memristance effects can explain spiketiming-dependent plasticity behaviour 34 . To date, many researchers have investigated memristor-based SNNs through full-system simulations using experimental device parameters 29,35,36 , although large-scale implementations are still limited.…”

Section: Nature Electronicsmentioning

confidence: 99%

The future of electronics based on memristive systems

2018

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Section: Nature Electronicsmentioning

confidence: 99%

The future of electronics based on memristive systems

2018

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“…It is important to predict and tailor the reaction of memristive devices to the action of real electrical signals, when trying to couple the memristive devices with biological cultures and tissues to organize an adaptive interface not only for the processing [49], but also for the classification and stimulation of the electrical activity of brain cells. There are a new physics and new applications behind such neurointerfaces on the basis of memristive devices.…”

Section: Noise-induced Phenomenamentioning

confidence: 99%

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

Mikhaylov

Gryaznov

Belov

et al. 2016

Phys. Status Solidi C

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The breakthrough in electronics and information technology is anticipated by the development of emerging memory and logic devices, artificial neural networks and brain-inspired systems on the basis of memristive nano-materials represented, in a particular case, by a simple 'metal-insulator-metal' (MIM) thin-film structure. The present article is focused on the comparative analysis of MIM devices based on oxides with dominating ionic (ZrOx, HfOx) and covalent (SiOx, GeOx) bonding of various composition and geometry deposited by magnetron sputtering. The studied memristive devices demonstrate reproducible change in their resistance (resistive switching - RS) originated from the formation and rupture of conductive pathways (filaments) in oxide films due to the electric-field-driven migration of oxygen vacancies and/or mobile oxygen ions. It is shown that, for both ionic and covalent oxides under study, the RS behaviour depends only weakly on the oxide film composition and thickness, device geometry (down to a device size of about 20x20 mu m(2)). The devices under study are found to be tolerant to ion irradiation that reproduces the effect of extreme fluences of high-energy protons and fast neutrons. This common behaviour of RS is explained by the localized nature of the redox processes in a nanoscale switching oxide volume. Adaptive (synaptic) change of resistive states of memristive devices is demonstrated under the action of single or repeated electrical pulses, as well as in a simple model of coupled (synchronized) neuron-like generators. It is concluded that the noise-induced phenomena cannot be neglected in the consideration of a memristive device as a nonlinear system. The dynamic response of a memristive device to periodic signals of complex waveform can be predicted and tailored from the viewpoint of stochastic resonance concept. (C) 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

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Section: Current State Of Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

Zhao

Tan

et al. 2020

Advanced Intelligent Systems

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The widespread implementation and rapid development of autonomous systems pose stringent performance requirements on emerging sensory systems. In addition to the basic sensing requirements, leading sensory systems are required to process data and extract featured information from highly redundant data in real time. With the added edge‐computational capabilities, data shuttling is avoided, leading to significant reduction of computational burden and bandwidth pressure in the cloud. Among the different computing architectures, the neuromorphic sensory system stands out due to its high power efficiency, low latency, and excellent processing capability. Mimicking the biological neural network, the colocation of sensory, processor, and memory components of neuromorphic sensory systems enables the requirements for frequent data shuttles to be circumvented. In particular, artificial intelligent perceptions built on memristive neuromorphic systems exhibit outstanding characteristics of small footprint, low power consumption, 3D stacking ability, and high density. Herein, the two essential parts of the memristive artificial perceptron system are presented: 1) memristive systems for neuromorphic computing and 2) high‐performance sensors. Next, the current state of the art established on artificial perceptron systems covering visual, auditory, and tactile sensations is highlighted. To conclude, the current challenges and future direction in the area of advanced intelligent perceptions are presented.

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Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Cited by 153 publications

References 45 publications

The future of electronics based on memristive systems

The future of electronics based on memristive systems

Field‐ and irradiation‐induced phenomena in memristive nanomaterials

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

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