A Memristor Neural Network Using Synaptic Plasticity and Its Associative Memory

Wang, Yabo; Wang, Guangyi; Shen, Yiran; Iu, Herbert Ho Ching

doi:10.1007/s00034-019-01330-8

Cited by 37 publications

(11 citation statements)

References 38 publications

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“…For this reason, it is believed that inputs with fast frequency can promote the fast growth of Ag CFs in the V2C, reducing the time of the integration process. In perspective, this property could be used to investigate the appropriate frequency of input signals in order to cut the power consumption for units of artificial neurons [52]. Biologically, the firing frequency increases with increased stimulus strength, called the strength-modulated spike frequency characteristic [53].…”

Section: Resultsmentioning

confidence: 99%

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

et al. 2021

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Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

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Section: Resultsmentioning

confidence: 99%

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

et al. 2021

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“…simulated the SRDP mechanism by employing phase transition memristors [26]. Despite these foundational advancements, the role of SRDP to implement advanced neural networks is rarely explored on oxide systems [27], [28]. Another shortcoming of STDP is that a pulse delay circuit is required in addition to the pulse generator to yield STDP spikes of appropriate delay.…”

Section: Efficient Resistive Switching and Spike Ratementioning

confidence: 99%

Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO₂ Memristor for Plausible Neuromorphic Systems

Boppidi

Suresh

Zhussupbekova

et al. 2020

IEEE Trans. Electron Devices

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In this work, we introduce a new class of p-type transparent conductive oxide (TCO) CuCrO 2 (150 nm) heterogeneously integrated onto FTO/glass for forming free memristor based neuromorphic applications. The fabricated Al/CuCrO 2 /FTO memristors demonstrate a reliable bipolar resistive switching with an ON/OFF ratio of 1000. The retention of the device was found to be steady even beyond 10 6 s, which demonstrates its non-volatility. The current-voltage (I-V) characteristics were fitted to evaluate its transport properties and a band-diagram was projected to have a better insight of the device operational principles. To validate the experimental observations, a new model has been developed, and the simulated I-V behavior was analogues to the experimental one. Efforts were then devoted to observe long-term potentiation (LTP) and longterm depression (LTD) utilizing identical but opposite pulses to evaluate the device's efficacy for synaptic applications. The synaptic behavior was well controlled by the pulse (pulse amplitude and width) variations. The conductance change was found to be symmetric and then saturated, which reflects the popular biological Hebbian rules. Finally, a long-term synaptic modulation has been implemented by establishing the spike rate dependent plasticity (SRDP) rule, which is a part of spiking neural networks and advantageous to mimic the brain's capability at low power. All the obtained experimental results were systematically corroborated by neural network simulation. Overall, our approach provides a new road map towards the development of TCO based alternative memristors, which can be employed to mimic the synaptic plasticity for energy-efficient bioinspired neuromorphic systems and non-Von Neumann computer architectures.

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“…The fast decay and slow decay of the read current are used to represent the STM and LTM characteristics in the ITO/CeO 2 ‐based memristor 18 . In addition to applying the single memristor to mimic the STM or LTM, the learning and forgetting behaviors of biological systems are emulated by using a memristor‐based neural network 22–26 . In 2015, Hu et al implemented the single‐associative memory and multiassociative memories with a memristive Hopfield network, which consists of nine memristors 22 .…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, Zeng et al proposed a memristive neural network with full‐function Pavlov associative memory with three interconnected neurons 24 . Wang et al proposed a memristor neural network to emulate the Pavlov's dog experiment including the learning, associative memory, and three kinds of forgetting behaviors, where CMOS transistors, capacitors, resistors, diodes, a memristor, and digital logic devices (AND gates, OR gates and NOT gates) to construct the neurons and synapses 25 . Despite there has been much effort in mimicking the learning and forgetting behaviors by applying memristors, a simple memristor‐based memory circuit that can realize different memory hierarchy levels of biological systems remains one issue.…”

Section: Introductionmentioning

confidence: 99%

Threshold‐type memristor‐based memory circuit

Liang

et al. 2021

Circuit Theory & Apps

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Summary Memory is of central importance to the research of high‐level cognitive behaviors of humans, where the plastic synapse contributes to the memory formation. Due to the controllable conductance and high‐density integration, the memristor acts as a promising candidate of artificial synapse. In terms of memory retention time, memory contains short‐term memory (STM) and long‐term memory (LTM). Additionally, the STM can be changed to the LTM via repeated learning, and this process is called memory consolidation. A threshold‐type memristor combined with basic operational circuits is used to design a memory circuit that can emulate various memory hierarchy levels containing the STM, the LTM, and the STM‐to‐LTM transition. Threshold firing behavior can also be emulated because of the threshold characteristic of the memristor. Furthermore, a floating emulator circuit that consists of several off‐the‐shelf devices is developed to mimic the electrical behaviors of the threshold‐type memristor, and its hardware circuit implementation is presented. Finally, both simulation and experimental results validate the feasibility and effectiveness of the proposed memory circuit.

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A Memristor Neural Network Using Synaptic Plasticity and Its Associative Memory

Cited by 37 publications

References 38 publications

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO₂ Memristor for Plausible Neuromorphic Systems

Threshold‐type memristor‐based memory circuit

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A Memristor Neural Network Using Synaptic Plasticity and Its Associative Memory

Cited by 37 publications

References 38 publications

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO2 Memristor for Plausible Neuromorphic Systems

Threshold‐type memristor‐based memory circuit

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Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO₂ Memristor for Plausible Neuromorphic Systems