Synaptic long-term potentiation realized in Pavlov's dog model based on a NiOx-based memristor

Hu, Shaoxiang; Liu, Y.; Liu, Z.; Chen, T. P.; Yu, Qin; Deng, Longjiang; Yin, You; Hosaka, Sumio

doi:10.1063/1.4902515

Cited by 54 publications

(32 citation statements)

References 37 publications

(53 reference statements)

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“…However, no obvious advantage can be found over synaptic devices based on bilayer and trilayer structures. Due to a simple structure, oxide single‐layers have also been widely used for synaptic devices, including AlO x , FeO x , HfO x , PrCaMnO x , SrTiO 3 , TaO x , TiO x , WO x , ZnHfO x , ZrHfO x , KNbO 3 , BiFeO 3 , SiO x , and NiO x . The working mechanism of PrCaMnO x ‐based memristive devices is widely attributed to field‐driven oxygen migration and redox reaction at a metal/PrCaMnO x interface .…”

Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

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be precisely regulated by the ionic flow, which is called synaptic plasticity. Generally, there are two types of synaptic plasticity: potentiation and depression. In the case of potentiation or depression, the synaptic weight increases or decreases upon repeated stimulation. Inspired by the human brain, novel non von Neumann computing architectures mainly composed of artificial synapses and artificial neurons have been proposed, which we can call "artificial neural networks," "brain-like computers," or "neuromorphic computers." [1,[3][4][5] However, the development of artificial brains that possess the efficiency of their biological counterparts in information processing remains a major challenge in computing. [6] One of the major challenges is the lack of suitable hardware to implement synapses, which are the main data processing elements in artificial brains. Generally, there are four essential requirements for an artificial synapse. [7,8] First, the analog weight should be nonvolatile in the absence of learning and weight updates should be bidirectional when the synapse is learning. Second, the synapse output is the product of the input signal and the synapse weight. Third, the weight updates vary with both the input signal and the stored weight value. Fourth, the synapse should be rather compact, and operate off a unidirectional information transfer with low power consumption.Mead and co-workers fabricated a four-terminal synaptic device based on a single floating gate silicon transistor in 1995. [8] It can simultaneously perform long term weight storage, compute the product of the input and floating gate value, and update the weight value according to a Hebbian or a backpropagation learning rule. In 1996, they developed a new floating-gate silicon transistor synapse with a threeterminal structure. [7] Hot-electron injection and electron tunneling make possible bidirectional weight updates. Given that these updates depend on both the stored weight value and the transistor terminal voltages, such synapse can implement a learning function. However, the integration density of transistor synapses is severely restricted to their three-or fourterminal structures. [6] The emergence of memristors endows artificial synapses with a new development opportunity. The memristor (short for memory resistor) is a two-terminal circuit element characterized by a relationship between the charge and the flux-linkage, which shows a distinctive "fingerprint" characterized by a pinched hysteresis loop confined to the first and the third quadrants of the Although the structure and function of the human brain are still far from being fully understood, brain-inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, go...

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Section: Working Mechanisms Of Memristive Synapsesmentioning

confidence: 99%

Memristive Synapses for Brain‐Inspired Computing

Wang

Zhuge

2019

Adv Materials Technologies

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“…However, few FETs are reported to mimic STDP learning behaviors . Besides, although some works have been reported to simulate classic conditioning, complex device circuits, materials and structure are the key to mimic classic conditioning in these references . Recently, wearable, printable and surrounding friendly electronics are attracting increasing interests, such as wearable sensors, skin electronics, etc.…”

Section: Introductionmentioning

confidence: 99%

Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

Zhu

Xiao

et al. 2018

Adv Funct Materials

142

149

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In neural system, both spike-timing-dependent plasticity (STDP) and conditioned reflex are vital synaptic learning mechanisms to regulate advanced neural activities, being associated with temporally coupled stimuli. Thus, realization of STDP and conditioned reflex on a single solid-state device may open up new opportunities for neuromorphic engineering. Here, restickable chitosan-gated oxide neuromorphic transistors are fabricated on polyimide tape. Based on protonic electrochemical doping/de-doping processes at indium-tin-oxide/chitosan interfaces, four types of STDP learning rules are successfully demonstrated, including Hebbian STDP, anti-Hebbian STDP, symmetrical STDP, and visual STDP. Trained with Hebbian STDP, Pavlovian associative learning and extinction behaviors are demonstrated successfully on a single-oxide neuromorphic transistor. No complex device circuits are needed. It is interesting to note here that the devices can be pasted on different holders with different curvature radii without degrading the transistor performances and STDP learning rules. Moreover, the proposed oxide neuromorphic transistors can be dissolved in deionized water easily. The results here indicate potential applications of the proposed restickable oxide neuromorphic transistors in flexible neuromorphic cognitive platforms.

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“…There have been many devices with different structures that successfully simulated synaptic dynamics, such as short-term plasticity (STP), long-term potentiation (LTP), and long-term depression (LTD) [16][17][18]. A MoS 2 / PTCDA hybrid heterojunction synapse has been demonstrated with efficient photoelectric dual modulation [10].…”

Section: Introductionmentioning

confidence: 99%

ReS2 Charge Trapping Synaptic Device for Face Recognition Application

et al. 2020

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Synaptic devices are necessary to meet the growing demand for the smarter and more efficient system. In this work, the anisotropic rhenium disulfide (ReS 2) is used as a channel material to construct a synaptic device and successfully emulate the long-term potentiation/depression behavior. To demonstrate that our device can be used in a large-scale neural network system, 165 pictures from Yale Face database are selected for evaluation, of which 120 pictures are used for artificial neural network (ANN) training, and the remaining 45 pictures are used for ANN testing. A three-layer ANN containing more than 10 5 weights is proposed for the face recognition task. Also 120 continuous modulated conductance states are selected to replace weights in our well-trained ANN. The results show that an excellent recognition rate of 100% is achieved with only 120 conductance states, which proves a high potential of our device in the artificial neural network field.

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Synaptic long-term potentiation realized in Pavlov's dog model based on a NiOx-based memristor

Cited by 54 publications

References 37 publications

Memristive Synapses for Brain‐Inspired Computing

Memristive Synapses for Brain‐Inspired Computing

Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

ReS2 Charge Trapping Synaptic Device for Face Recognition Application

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