Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity

Yang, Yuchao; Chen, Bing; Lü, Wei

doi:10.1002/adma.201503202

Cited by 143 publications

(150 citation statements)

References 33 publications

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“…The distant parts of the dendrites of excitatory neurons in the brain are also often considered modulatory as inputs at these locations often cannot cause the neurons to fire but can control the efficacy and plasticity of synaptic inputs closer to the soma. [275] [273] This mechanism potentially affords the finest level of control of plasticity.…”

Section: Control Of Plasticitymentioning

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: Control Of Plasticitymentioning

confidence: 99%

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

et al. 2019

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“…Since its theoretical inception in 1970s1718, followed by the establishment of connection with resistance switching behaviour in 2008 (ref. 19), memristive systems have attracted extensive research interests as a disruptive technology for nonvolatile memory92021, in-memory logic22 and brain-inspired computing232425. Depending on the moving ion species, memristive systems can be further classified into electrochemical metallization memory (ECM) driven by the transport of metal cations1326 and valence change memory (VCM) where the resistance switching is presumably caused by the migration of oxygen ions/vacancies92728.…”

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confidence: 99%

Probing nanoscale oxygen ion motion in memristive systems

et al. 2017

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Ion transport is an essential process for various applications including energy storage, sensing, display, memory and so on, however direct visualization of oxygen ion motion has been a challenging task, which lies in the fact that the normally used electron microscopy imaging mainly focuses on the mass attribute of ions. The lack of appropriate understandings and analytic approaches on oxygen ion motion has caused significant difficulties in disclosing the mechanism of oxides-based memristors. Here we show evidence of oxygen ion migration and accumulation in HfO2 by in situ measurements of electrostatic force gradient between the probe and the sample, as systematically verified by the charge duration, oxygen gas eruption and controlled studies utilizing different electrolytes, field directions and environments. At higher voltages, oxygen-deficient nano-filaments are formed, as directly identified employing a CS-corrected transmission electron microscope. This study could provide a generalized approach for probing ion motions at the nanoscale.

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“…It has been found that heterosynaptic plasticity enhances synaptic competition and prevents synaptic weight from going to the extremes to keep the neural network stable. [201,202] In the scheme of three-terminal devices, Yang et al [203] first reported heterosynaptic facilitation in 2015, as shown in Figure 15. Till now, two schemes have been proposed to implement the heterosynaptic plasticity in memristive devices: one is adding another modulatory terminal to the normal pre-/post-synaptic terminal to form three-terminal devices, [200] and the other one is utilizing multiple terminal devices to realize heterosynaptic cooperation and competing.…”

Section: Heterosynaptic Plasticitymentioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

174

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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Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity

Cited by 143 publications

References 33 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

Probing nanoscale oxygen ion motion in memristive systems

Memristive Synapses and Neurons for Bioinspired Computing

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