Artificial Neuron with Spike Frequency Adaptation Based on Mott Memristor

Wei, Qiumeng; Tang, Jianshi; Zhong, Ya‐Nan; Gao, Bin; Qian, He

doi:10.1109/edtm50988.2021.9421014

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…These years, more attention is focused on modifications of the LIF model to achieve more biological neuron functions with lower hardware cost and energy consumption. Liu et al [128] achieved output frequency adaption (homeostasis function) by connecting an analog memristor under the influence of continuous input pulses in series to the neuron circuit. Later, Wei et al [129] reported the similar function more reliably using an additional transistor circuit (Fig.…”

Section: Artificial Neuronmentioning

confidence: 99%

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Zuo¹,

Fu²,

Zhang³

et al. 2023

J. Semicond.

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With rapid advancement and deep integration of artificial intelligence and the internet-of-things, artificial intelligence of things has emerged as a promising technology changing people’s daily life. Massive growth of data generated from the devices challenges the AIoT systems from information collection, storage, processing and communication. In the review, we introduce volatile threshold switching memristors, which can be roughly classified into three types: metallic conductive filament-based TS devices, amorphous chalcogenide-based ovonic threshold switching devices, and metal-insulator transition based TS devices. They play important roles in high-density storage, energy efficient computing and hardware security for AIoT systems. Firstly, a brief introduction is exhibited to describe the categories (materials and characteristics) of volatile TS devices. And then, switching mechanisms of the three types of TS devices are discussed and systematically summarized. After that, attention is focused on the applications in 3D cross-point memory technology with high storage-density, efficient neuromorphic computing, hardware security (true random number generators and physical unclonable functions), and others (steep subthreshold slope transistor, logic devices, etc.). Finally, the major challenges and future outlook of volatile threshold switching memristors are presented.

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Section: Artificial Neuronmentioning

confidence: 99%

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Zuo¹,

Fu²,

Zhang³

et al. 2023

J. Semicond.

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“…文献 [17]主要采用两个纳米级VO 2 LAM和两个电容构建了HH (Hodgkin-Huxley)神经元模型 , 实验结果表明其具有 23种神经形态行为. 文献 [18]中提出了一个非易失的LAM数学模型, 并将其应用在HR (Hindmarsh-Rose)神经元中, 可产生四种共存的神经脉冲行为. 文献 [19]基于NbO x LAM设计了一种LIF (leaky integrate-and-fire)神经元, 并将SFA (spike frequency adaptation) 行为整合到神经元中, 建立了相应的自适应神经元模型.…”

Section: 奠定了理论基础unclassified

Design of NbO<sub><i>x</i></sub> memristive neuron and its application in spiking neural networks

古亚娜¹,

梁燕²,

王光义³

et al. 2022

Acta Phys. Sin.

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NbOx memristors show great application prospect in neuromorphic computing due to its nanoscale size, threshold switching, and locally active properties. The in-depth analysis and study of NbOx memristors's dynamic properties are beneficial to the design and optimization of memristive neuron circuits. In this paper, based on the local active theory, the physical model of NbOx memristor is studied by using the small signal analysis method, and the region and conditions of the peak oscillation are quantitatively analyzed, and the quantitative relationship between the excitation signal amplitude and the peak frequency is determined. Based on the above theoretical analysis, NbOx memristor neurons are further designed, and combined with the memristive synaptic crisscross array, to construct a 25×10 spiking neural network (SNN). Finally, the recognitional function of digital 0 to 9 patterns are effectively realized by using frequency coding and time coding respectively.

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“…In contrast to the human visual system, existing visual perception systems use completely different architectures, in which sensors, memories, and computing units are separated from each other [3][4][5]. The information flows as follows: sensors perceive external optical signals and convert them into electrical analog signals with a lot of redundant information; after conversion by an analog-digital converter and amplification by an amplifier, the digital signals are transmitted to remote computing units for further processing [6,7].…”

Section: Introductionmentioning

confidence: 99%

Artificial visual neuron based on threshold switching memristors

Wen

Zhu²,

Guo³

2023

Neuromorph. Comput. Eng.

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The human visual system encodes optical information perceived by photoreceptors in the retina into neural spikes and then processes them by the visual cortex, with high efficiency and low energy consumption. Inspired by this information processing mode, an universal artificial neuron constructed with a resistor (Rs) and a threshold switching memristor can realize rate coding by modulating pulse parameters and the resistance of Rs. Owing to the absence of an external parallel capacitor, the artificial neuron has minimized chip area. In addition, an artificial visual neuron is proposed by replacing Rs in the artificial neuron with a photo-resistor. The oscillation frequency of the artificial visual neuron depends on the distance between the photo-resistor and light, which is fundamental to acquiring depth perception for precise recognition and learning. A visual perception system with the artificial visual neuron can accurately and conceptually emulate the self-regulation process of the speed control system in driverless automobiles. Therefore, the artificial visual neuron can process efficiently sensory data, reduce or eliminate data transfer and conversion at sensor/processor interfaces, and expand its application in the field of artificial intelligence.

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Artificial Neuron with Spike Frequency Adaptation Based on Mott Memristor

Cited by 5 publications

References 7 publications

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Volatile threshold switching memristor: An emerging enabler in the AIoT era

Design of NbO<sub><i>x</i></sub> memristive neuron and its application in spiking neural networks

Artificial visual neuron based on threshold switching memristors

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