Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing

Barbera, Selina La; Vincent, Adrien F.; Vuillaume, D.; Querlioz, Damien; Alibart, Fabien

doi:10.1038/srep39216

Cited by 28 publications

(18 citation statements)

References 36 publications

(47 reference statements)

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“…Volatility of resistive switching in Ag‐based ECM threshold switches has been reported for realizing neuronal functions like short‐term synaptic plasticity (STP), short‐term plasticity to long‐term plasticity (LTP) transition, as well as spike‐timing‐dependent plasticity (STDP) …”

Section: Applicationsmentioning

confidence: 99%

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

259

231

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Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.is applied due to the formation of a conduction channel(s) with Ag or Cu atoms. Unlike the ECM cells, the resistance recovers back spontaneously upon cessation of the external bias, yielding a superior I-V nonlinearity [4][5][6][7][8][9][10][11] and unique temporal conductance evolution dynamics. [7,12,13] Such a relaxation process is due to the physical dissolution of the metallic conduction channel under driving forces such as minimization of interfacial energy. In case active metals are used as electrodes, these metals may be doped into the dielectrics eventually under the combined effect of electric fields, thermal diffusion, which may lead to a reduced threshold voltage for the subsequent switching, similar to the process called "electroforming." The unique delay and relaxation dynamics of Ag and Cu-based threshold switches make them suitable for innovative applications in circuits and systems.Threshold switches with Ag or Cu active metals are also termed as "diffusive memristors" [7] to emphasize the underlying nature of the diffusive dynamics of the metal species. Factors including bias amplitude, biasing duration, as well as ambient temperature have been observed to have an impact on such a process, showing a wide range of dynamical properties, which could be exploited as access devices for memories with fast transition (e.g., <100 ns) or synaptic emulators with a relatively slower evolution (e.g., >1 µs). We survey the recently developed material systems which have exhibited this kind of threshold switching. New evidences by electron microscopy and quantitatively thermodynamic modeling are examined to give insights into the underlying physics of the mechanisms. We also discuss applications enabled by the advent of such threshold switches. Temporal Response of the SwitchingThe dynamical response of threshold switching is a critical property for many applications but has been characterized to a lesser extent. The temporal responses could be probed by applying voltage pulses and measuring the resulting currents in the time domain. It is a general observation in both ECM and threshold switches that the conductance experienced a transition from insulating state to conducting state after a finite time duration (delay time) under the external bias, as ill...

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Section: Applicationsmentioning

confidence: 99%

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

259

231

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“…It complies with neuromorphic circuits and in particular with scalable nonvolatile resistive memory (memristive) devices that can mimic artificial synapses with embedded STDP plasticity and offer the perspective of very-lowpower implementation of spiking neural networks in analog hardware for pattern recognition. [4][5][6][7][8][9][10]35,63,64 Indeed, synapses are several orders of magnitude more numerous than neurons in neural networks. Thus, low-power computing based on artificial neural networks should be conceived to be compatible with low-power synapses.…”

Section: Discussionmentioning

confidence: 99%

“…Both have been modeled into RRAM synapses, making the method compliant with currently developed nanoscale neuromorphic hardware. 10,15,65,66 The more power-consuming part of our network is the synaptic connection between the input layer and the intermediate layer with 8 * 10 6 spikes per second generated by the input layer, each transmitted through 60 synapses. With a raw estimation of about 200 pJ of energy consumed for each weight change, 57,67 this leads to a total power of less than 10 µW.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, they offer an opportunity to be implemented in very-low-power analog neuromorphic hardware that currently undergoes important developments, in particular with memristive devices that are able to mimic synaptic plasticity, such as spike-timing-dependent plasticity (STDP), at a highly miniaturized scale. [4][5][6][7][8][9][10][11] STDP rules are local learning rules that change the synapse weight depending on the time difference between pre-and post-synaptic spikes. It has been shown that incorporating such rule in SNNs allows to perform unsupervised learning, 12,13 and this strategy has been successfully applied for visual or auditory pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

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An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

Bernert

Yvert

2019

Int. J. Neur. Syst.

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Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.

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“…The nonvolatile resistive switching properties of a memristor simulate the synaptic plasticity and reduce power consumption. Although all types of memristors can be used as synapse in neuromorphic computing, different types of memristors have different synaptic plasticity mimicking characteristics such as the LTP, STP, and stochastic activation . Besides, ANNs and SNNs require different types of synapse.…”

Section: Synaptic Memristormentioning

confidence: 99%

Neuromorphic Computing with Memristor Crossbar

Zhang

Huang

et al. 2018

Physica Status Solidi (a)

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Neural networks, one of the key artificial intelligence technologies today, have the computational power and learning ability similar to the brain. However, implementation of neural networks based on the CMOS von Neumann computing systems suffers from the communication bottleneck restricted by the bus bandwidth and memory wall resulting from CMOS downscaling. Consequently, applications based on large-scale neural networks are energy/ area hungry and neuromorphic computing systems are proposed for efficient implementation of neural networks. Neuromorphic computing system consists of the synaptic device, neuronal circuit, and neuromorphic architecture. With the two-terminal nonvolatile nanoscale memristor as the synaptic device and crossbar as parallel architecture, memristor crossbars are proposed as a promising candidate for neuromorphic computing. Herein, neuromorphic computing systems with memristor crossbars are reviewed. The feasibility and applicability of memristor crossbars based neuromorphic computing for the implementation of artificial neural networks and spiking neural networks are discussed and the prospects and challenges are also described.

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Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing

Cited by 28 publications

References 36 publications

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

Neuromorphic Computing with Memristor Crossbar

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