Synaptic Suppression Triplet‐STDP Learning Rule Realized in Second‐Order Memristors

Yang, Rui; Huang, Heming; Hong, Qinghui; Yin, Xue-Bing; Tan, Zhiqiang; Shi, Tao; Zhou, Yi; Miao, Xiangshui; Wang, Xiaoping; Mi, Shao‐Bo; Jia, Chun‐Lin; Guo, Xin

doi:10.1002/adfm.201704455

Cited by 213 publications

(164 citation statements)

References 51 publications

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“…[234] Experimentally, the implementation of STDP using memristor typically relies on the overlap between pre-and postspikes applied on its two terminals. [92,238] In addition, certain memristor dynamics could provide a timing mechanism, from which STDP with non-overlapping spikes has also been realized.…”

Section: Hebbian Learningmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

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Section: Hebbian Learningmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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“…[171,174] It should be noted that the STDP rules realized in these studies still falls within the concept of classical pair-STDP, since they assume that all pre-/post-synaptic pairs contribute independently to the modulation of synaptic weight. Recently Yang and her coworkers [176] experimentally realized suppression triplet-STDP in STO second-order memristive devices with forgetting effect, as shown in Figure 14. This simplicity causes the pair-STDP to fail to capture some biological synaptic plasticity, such as the spiking rate-dependent plasticity.…”

Section: Wwwadvelectronicmatdementioning

confidence: 98%

“…[178] In addition to suppression triplet-STDP, the other triplet-STDP is coactivation and timing-dependent integration rule proposed by Wang et al [35] This form of triplet-STDP has also been implemented in TiO 2 memristive devices. [176] Copyright 2018, Wiley-VCH. Electron.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

“…In 2018, Yang et al [176] designed an LIF neuron in which an ideally volatile memristive device (Memristor 2) was adopted to represent membrane potential with short-term dynamics and thus realize leaky function. In 2018, Yang et al [176] designed an LIF neuron in which an ideally volatile memristive device (Memristor 2) was adopted to represent membrane potential with short-term dynamics and thus realize leaky function.…”

Section: Bioplausible Memristive Neuronsmentioning

confidence: 99%

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Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

Self Cite

186

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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“…Equation (2) is the dynamic equation of w that leads to synaptic plasticity. Recent studies, [71,72,80,81] however, show that w is also dependent on other "second-order" state variables such as the ion mobility [72] and local temperature. Recent studies, [71,72,80,81] however, show that w is also dependent on other "second-order" state variables such as the ion mobility [72] and local temperature.…”

Section: Homosynaptic Plasticitymentioning

confidence: 99%

Nanoionic Resistive‐Switching Devices

Zhu

Lee

Lü

2019

Adv Elect Materials

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devices have opened up promising opportunities for electronic circuit applications beyond energy storage. One representative example is ionic resistive-switching (RS) devices (also called memristive devices or memristors), [3][4][5] which can be used as a nonvolatile memory element due to its excellent performance such as high switching speed (<1 ns), [6] good retention (>10 years), [7] good endurance (>10 9 ), [8] as well as high device scalability. [9] The RS effect refers to the phenomenon that the resistance of a dielectric thin film, typically sandwiched between two electrodes, can be reversibly modulated by an electric field. [2] In ionic RS devices, the mechanism originates from the rearrangement of atoms/ions in the dielectric thin film through ionic drift/ diffusion and electrochemical processes that lead to the formation/rupture of nanoscale conductive paths (filaments) between the two electrodes. [10] Controlling these internal ionic processes will enable the design and implementation of better engineered devices with improved performance and reliability. Additionally, recent studies show that the internal ionic processes during RS can be used to faithfully emulate many biological effects that are critical for learning and memory, allowing efficient neuromorphic systems to be implemented using solid-state devices and networks. [11][12][13] Controlled ionic processes can also be used to directly modify the composition and/or structure of the material itself at the atomic scale, allowing new nanostructures to be built on the fly, in a reconfigurable fashion.In this review, we will first discuss the switching mechanism of ion-induced RS (memristive) effects in nanoscale solid-state thin films, followed by discussions on controlling and utilization of the ionic effects to implement biological synaptic functions, and to reduce device variation and improve device stability. New concepts of material tuning enabled by controlled ionic process will be introduced at the end. Ion-Induced RS EffectSo far, ion-driven RS effects have been observed in a large variety of materials, ranging from oxides, halides, to chalcogenides, etc. [2,[13][14][15] Based on the types of ions involved during the RS process, the switching mechanism can be divided to three categories: cation-driven RS effects, anion-driven RS effects, and cation and anion jointly driven RS effects.Advances in the understanding of nanoscale ionic processes in solid-state thin films have led to the rapid development of devices based on coupled ionic-electronic effects. For example, ion-driven resistive-switching (RS) devices have been extensively studied for future memory applications due to their excellent performance in terms of switching speed, endurance, retention, and scalability. Recent studies further suggest that RS devices are more than just resistors with tunable resistance; instead, they exhibit rich and complex internal ionic dynamics that equip them with native information-processing capabilities, particularly in the temporal domain. RS effec...

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Synaptic Suppression Triplet‐STDP Learning Rule Realized in Second‐Order Memristors

Cited by 213 publications

References 51 publications

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Memristive Synapses and Neurons for Bioinspired Computing

Nanoionic Resistive‐Switching Devices

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