A carbon-based memristor design for associative learning activities and neuromorphic computing

Pei, Yifei; Zhou, Zhenyu; Chen, Andy Paul; Chen, Jingsheng; Yan, Xiaobing

doi:10.1039/d0nr02894k

Cited by 55 publications

(52 citation statements)

References 65 publications

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“…From Fig. S11c, d, it can be found that the conductance can be continuously changed, and the sandwich structure of the device can be viewed as a pre-and post-synaptic neuron and synapse, with Ag ions as neurotransmitters to simulate the change of biological synaptic weights, thereby simulating the function of biological neurons [37]. Further, biological neurological memory is divided into STM and LTM, while STM can form LTM by repeated stimulation, as shown in the model diagram illustrated (Fig.…”

Section: Resultsmentioning

confidence: 99%

Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

Zhao

Pei

Zhang³

et al. 2022

Sci. China Mater.

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Nowadays, memristors are extremely similar to biological synapses and can achieve many basic functions of biological synapses, making them become a new generation of research hotspots for brain-like neurocomputing. In this work, we prepare a memristor based on two-dimensional α-In 2 Se 3 nanosheets, which exhibits excellent electrical properties, faster switching speeds, and continuous tunability of device conduction. Meanwhile, most basic bio-synapse functions can be implemented faithfully, such as short-term memory (STM), long-term memory (LTM), four different types of spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF). More importantly, we systematically study three effective methods to achieve LTM, in which the reinforcement learning can be faithfully simulated according to the Ebbinghaus forgetting curve. Therefore, we believe this work will promote the development of learning functions for brain-like computing and artificial intelligence.

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Section: Resultsmentioning

confidence: 99%

Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

Zhao

Pei

Zhang³

et al. 2022

Sci. China Mater.

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“…where G 1 and G 2 are the conductance read from the front and rear low pulses, respectively [38]. The two fitting times τ 1 (1.22 ns) and τ 2 (131 ns) represent the fast and slow decaying items, respectively.…”

Section: Resultsmentioning

confidence: 99%

Artificial nociceptor based on TiO2 nanosheet memristor

et al. 2021

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With the development of technology, the learning and memory functions of artificial memristor synapses are necessary for realizing artificial neural networks and neural neuromorphic computing. Owing to their high scalability performance, nanosheet materials have been widely employed in cellular-level learning, but the behaviors of nociceptor based on nanosheet materials have rarely been studied. Here, we present a memristor with an Al/TiO 2 /Pt structure. After electroforming, the memristor device showed a gradual conductance regulation and could simulate synaptic functions such as the potentiation and depression of synaptic weights. We also designed a new scheme that verifies the pain sensitization, desensitization, allodynia, and hyperalgesia behaviors of real nociceptors in the fabricated memristor. Memristors with these behaviors can significantly improve the quality of intelligent electronic devices. Data fitting showed that the high resistance and low resistance states were consistent with the hopping conduction mechanism. This work promises the application of TiO 2-based devices in next-generation neuromorphological systems.

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“…With the development of neuroscience, there are three more STDP forms that have been presented, including asymmetric anti‐Hebbian STDP, symmetric Hebbian STDP, and symmetric anti‐Hebbian STDP. [ 99,102 ] The weight updates of asymmetric and symmetric STDP can be expressed as

Δ w = {\begin{matrix} A e^{- Δ t false/ τ} + Δ w_{0} & asymmetric STDP \\ A e^{- Δ t^{2} false/ τ^{2}} + Δ w_{0} & symmetric STDP \end{matrix}

where

Δ t

is the time delay between pre‐ and postsynaptic spikes, A and τ are the scaling factor and time constant, and

w_{0}

is the constant representing a nonassociative component of the synaptic change.…”

Section: Artificial Synapses and Neuronsmentioning

confidence: 99%

Exploring Phase‐Change Memory: From Material Systems to Device Physics

Ren

Sun

Chen³

et al. 2021

Physica Rapid Research Ltrs

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To deal with the growing demand for data storage and processing, phase‐change memory (PCM) provides one of the most promising candidates for next‐generation nonvolatile data storage and neuromorphic computing applications. A lot of effort has been made toward optimizing the materials and device design; thus, excellent device performances have been achieved including high density, fast switching speed, great endurance, and retention. In addition, the widely tunable optical characteristics of PCMs are irresistibly attractive for optoelectronic or all‐optical applications with unprecedented bandwidth, low energy consumption, and multilevel data storage. Herein, the materials system and switching mechanisms on experimental and modeling methods for PCM designs and applications are discussed. Electric‐domain and optical‐domain PCM‐based artificial synapses/neurons and their applications in neuromorphic computing are also reviewed. Finally, the future prospects and challenges of PCM‐based applications on materials, devices, algorithms, and system levels are highlighted.

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A carbon-based memristor design for associative learning activities and neuromorphic computing

Abstract:
A model based on carbon conductive filaments (CFs) for a memristor based on carbon quantum dots (QDs) is proposed for the first time.

Cited by 55 publications

References 65 publications

Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

Artificial nociceptor based on TiO2 nanosheet memristor

Exploring Phase‐Change Memory: From Material Systems to Device Physics

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A carbon-based memristor design for associative learning activities and neuromorphic computing

Abstract: A model based on carbon conductive filaments (CFs) for a memristor based on carbon quantum dots (QDs) is proposed for the first time.

Cited by 55 publications

References 65 publications

Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior

Artificial nociceptor based on TiO2 nanosheet memristor

Exploring Phase‐Change Memory: From Material Systems to Device Physics

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Product

Resources

About

Abstract:
A model based on carbon conductive filaments (CFs) for a memristor based on carbon quantum dots (QDs) is proposed for the first time.