基于二维材料MXene的仿神经突触忆阻器的制备和长/短时程突触可塑性的实现

兼具长时程可塑性与短时程可塑性的电子突触被认为是类脑计算系统的重要基础.将一种新型二维材料MXene应用到忆阻器中,制备了基于Cu/MXene/SiO_2/W的仿神经突触忆阻器.结果表明, Cu/MXene/SiO_2/W忆阻器成功实现了稳定的双极性模拟阻态切换,同时成功模拟了生物突触短时程可塑性的双脉冲易化功能和长时程可塑性的长期增强/抑制行为,其中双脉冲易化的易化指数与脉冲间隔时间相关. Cu/MXene/SiO_2/W忆阻器的突触仿生特性,归功于MXene辅助的Cu离子电导丝形成与破灭的类突触响应机理.由于Cu/MXene/SiO_2/W忆阻器兼具长时程可塑性与短时程可塑性,其在突触仿生电子学和类脑智能领域将会具有巨大的应用前景.     

Electronic synapses based on ultrathin quasi-two-dimensional gallium oxide memristor

Synapse emulation is very important for realizing neuromorphic computing, which could overcome the energy and throughput limitations of today's computing architectures. Memristors have been extensively studied for using in nonvolatile memory storage and neuromorphic computing. In this paper, we report the fabrication of vertical sandwiched memristor device using ultrathin quasi-two-dimensional gallium oxide produced by squeegee method. The as-fabricated two-terminal memristor device exhibited the essential functions of biological synapses, such as depression and potentiation of synaptic weight, transition from short time memory to long time memory, spike-timing-dependent plasticity, and spike-rate-dependent plasticity. The synaptic weight of the memristor could be tuned by the applied voltage pulse, number,width, and frequency. We believe that the injection of the top Ag cations should play a significant role for the memristor phenomenon. The ultrathin of medium layer represents an advance to integration in vertical direction for future applications and our results provide an alternative way to fabricate synaptic devices.     

Impact of Ta/Ti electrodes on linearities of TaO_x-based resistive random-access memories for neuromorphic computing

In this work, by incorporating different electrodes(Ta/Ti) onto TaOxdielectric layer, we studied both the conductance reading and conductance updating(long term potentiation and depression) linearities in the two RRAM devices. Owing to the composition modulation(CM) mechanism, the Ta-electrode device shows better conductance reading and updating linearities. The RRAM device linearities directly influence the performance of the neural network when the devices are used as synapses. System evaluation of a two-layer neural network considering the conductance reading and updating linearity factors further confirm that both the training and inference accuracies of Ta electrode device are better than those of the Ti electrode one. We believe that this work could serve as a powerful reference for engineering synaptic devices with good linearity for neuromorphic computing applications.     

Multilevel resistive switching and synaptic behaviors in MnO-based memristor

Exploring new synaptic electronic devices that combine computing and memory is a promising strategy that fundamentally approaches intelligent machines. In this study, the multilevel resistive switching and synaptic behaviors of a MnO-based device is studied. The device is composed of Al/MnO/Ni sandwich structure, has stable resistance switching characteristics, has continuous nonvolatile memory state, can be used as electrically programmable and erasable analog memory. The gradual conductance modulation is realized by changing the compliance current and the maximum scanning voltage. The Al/MnO/Ni devices successfully mimic the basic functions of synapses, including the paired-pulse facilitation, spike-rate-dependent plasticity, excitatory postsynaptic current, short-term plasticity, long-term plasticity, and sike-timing-dependent plasticity.     

Synaptic behaviors and modeling of a metal oxide memristive device

Nanoscale memristive devices using tungsten oxide as the switching layer have been fabricated and characterized. The devices show the characteristics of a flux-controlled memristor such that the conductance change is governed by the history of the applied voltage signals, leading to synaptic behaviors including long-term potentiation and depression. The memristive behavior is attributed to the migration of oxygen vacancies upon bias which modulates the interplay between Schottky barrier emission and tunneling at the WO _X /electrode interface. A physical model incorporating ion drift and diffusion effects using an internal state variable representing the area of the conductive region has been proposed to explain the observed memristive behaviors. A SPICE model has been further developed that can be directly incorporated into existing circuit simulators. This type of device can be fabricated with low-temperature processes and has potential applications in synaptic computations and as analog circuit components.     

Bi-directional modulation of AMPA receptor unitary conductance by synaptic activity

Background  

Knowledge of how synapses alter their efficiency of communication is central to the understanding of learning and memory. The most extensively studied forms of synaptic plasticity are long-term potentiation (LTP) and its counterpart long-term depression (LTD) of AMPA receptor-mediated synaptic transmission. In the CA1 region of the hippocampus, it has been shown that LTP often involves a rapid increase in the unitary conductance of AMPA receptor channels. However, LTP can also occur in the absence of any alteration in AMPA receptor unitary conductance. In the present study we have used whole-cell dendritic recording, failures analysis and non-stationary fluctuation analysis to investigate the mechanism of depotentiation of LTP.     

Memristors Constructed by CsPbIBr2 Inorganic Halide Perovskite for Artificial Synapse and Logic Operation

Neuromorphic devices are one of the promising electronic devices that are implementing artificial neural networks and substituting for traditional semiconductor devices in recent years. Inorganic halide perovskite (IMHP) is considered as an advantageous material to constitute neuromorphic components. Herein, the CsPbIBr₂ memristor displays superior resistive-switching properties under various temperature and storage periods. In addition, synaptic plasticity, including paired pulse facilitation and spiking timing-dependent plasticity, is observed for CsPbIBr₂ device, whose resistance manipulation is also established in both DC and pulse modes. Moreover, the decimal operation function of numbers by applying pulse stimulation to the device to regulate the device conductance is realized. This work demonstrates the feasibility of IMHP in neuromorphic devices, accelerating the application of neuromorphic computing.     

Spiking sychronization regulated by noise in three types of Hodgkin—Huxley neuronal networks

In this paper,we study spiking synchronization in three different types of Hodgkin-Huxley neuronal networks,which are the small-world,regular,and random neuronal networks.All the neurons are subjected to subthreshold stimulus and external noise.It is found that in each of all the neuronal networks there is an optimal strength of noise to induce the maximal spiking synchronization.We further demonstrate that in each of the neuronal networks there is a range of synaptic conductance to induce the effect that an optimal strength of noise maximizes the spiking synchronization.Only when the magnitude of the synaptic conductance is moderate,will the effect be considerable.However,if the synaptic conductance is small or large,the effect vanishes.As the connections between neurons increase,the synaptic conductance to maximize the effect decreases.Therefore,we show quantitatively that the noise-induced maximal synchronization in the Hodgkin-Huxley neuronal network is a general effect,regardless of the specific type of neuronal network.     

Recent progress on two-dimensional neuromorphic devices and artificial neural network

Mimicking biological synapses with microelectronic devices is widely considered as the first step in hardware building artificial neuromorphic networks, which is also the basis of brain-inspired neuromorphic computing. Numerous artificial neurons and synapses making up an artificial neuromorphic network have been gained wide attention due to their powerful and efficient data processing capabilities. Recently, artificial synapses, especially memristor-type and transistor-type synapses based on multifarious two-dimensional (2D) materials have been paid much attention. The unique properties of 2D materials make devices perform well in learning ability and power efficiency when mimicking synaptic behaviors, which highlights the feasibility of 2D neuromorphic devices in constructing artificial neuromorphic networks. Herein, the basic structures and principles of biological synapses are introduced, and the definitions of synaptic behaviors in synaptic electronic devices are discussed. Then, the progress of 2D memristor-type and transistor-type neuromorphic devices involving their device architecture, neuromorphic operational mechanism, and promising applications is reviewed. Finally, the future challenges of artificial synaptic devices based on 2D materials are discussed briefly.     

Brain-like synaptic memristor based on lithium-doped silicate for neuromorphic computing

Artificial synapse is one of the potential electronics for constructing neural network hardware. In this work, Pt/LiSiO_x/TiN analog artificial synapse memristor is designed and investigated. With the increase of compliance current (C. C.) under 0.6 mA, 1 mA, and 3 mA, the current in the high resistance state (HRS) presents an increasing variation, which indicates lithium ions participates in the operation process for Pt/LiSiO_x/TiN memristor. Moreover, depending on the movement of lithium ions in the functional layer, the memristor illustrates excellent conduction modulation property, so the long-term potentiation (LTP) or depression (LTD) and paired-pulse facilitation (PPF) synaptic functions are successfully achieved. The neural network simulation for pattern recognition is proposed with the recognition accuracy of 91.4%. These findings suggest the potential application of the LiSiO_x memristor in the neuromorphic computing.     

基于忆阻器的spiking神经网络在图像边缘提取中的应用

根据生物视觉系统的功能原理,用忆阻器模拟生物突触,结合忆阻器的记忆特性和spiking神经网络的高效处理能力,构造了一种可用于图像边缘提取的三层spiking神经网络模型,该网络用忆阻器电导的变化量来表征图像边缘信息,仿真结果表明,该方法的边缘提取结果具有连续性、光滑性、低误检漏检性和边缘定位准确性,该神经网络的处理过程符合生物信息处理机制,为视觉系统的仿生实现提供了新的思路。     

基于六角氮化硼二维薄膜的忆阻器

报道了一种基于多层六角氮化硼(h-BN)二维薄膜的忆阻器件.该器件不需要电预处理过程,且具有自限流的双极性阻变行为;具有较好的抗疲劳性和较长的数据保持时间.该器件在脉冲编程条件下具有模拟转变特性,即在连续的电压脉冲下器件的电阻态能被连续地调控,使得该器件能够模仿神经网络系统中的神经突触权重变化行为.综上所述,基于多层h-BN的忆阻器具有应用在非易失性存储和神经计算中的潜力.     

Mean Field Theory of Self-Organizing Memristive Connectomes

Biological neuronal networks are characterized by nonlinear interactions and complex connectivity. Given the growing impetus to build neuromorphic computers, understanding physical devices that exhibit structures and functionalities similar to biological neural networks is an important step toward this goal. Self-organizing circuits of nanodevices are at the forefront of the research in neuromorphic computing, as their behavior mimics synaptic plasticity features of biological neuronal circuits. However, an effective theory to describe their behavior is lacking. This study provides for the first time an effective mean field theory for the emergent voltage-induced polymorphism of circuits of a nanowire connectome, showing that the behavior of these circuits can be explained by a low-dimensional dynamical equation. The equation can be derived from the microscopic dynamics of a single memristive junction in analytical form. The effective model is tested on experiments of nanowire networks and show that it fits both the potentiation and depression of these synapse-mimicking circuits. It is shown that this theory applies beyond the case of nanowire networks by formulating a general mean-field theory of conductance transitions in self-organizing memristive connectomes.     

Thermal impact on spiking properties in Hodgkin-Huxley neuron with synaptic stimulus

The effect of environmental temperature on neuronal spiking behaviors is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin-Huxley neuron subject to synaptic stimulus. We find that the spiking threshold exhibits a global minimum in a specific temperature range where spike initiation needs weakest synaptic strength, which form the engineering perspective indicates the occurrence of optimal use of synaptic transmission in the nervous system. We further explore the biophysical origin of this phenomenon associated with ion channel gating kinetics and also discuss its possible biological relevance in information processing in neuronal systems.      

Synaptic plasticity and classical conditioning mimicked in single indium-tungsten-oxide based neuromorphic transistor

Emulation of synaptic function by ionic/electronic hybrid device is crucial for brain-like computing and neuromorphic systems. Electric-double-layer(EDL) transistors with proton conducting electrolytes as the gate dielectrics provide a prospective approach for such application. Here, artificial synapses based on indium-tungsten-oxide(IWO)-based EDL transistors are proposed, and some important synaptic functions(excitatory post-synaptic current, paired-pulse facilitation,filtering) are emulated. Two types of spike-timing-dependent plasticity(Hebbian STDP and anti-Hebbian STDP) learning rules and multistore memory(sensory memory, short-term memory, and long-term memory) are also mimicked. At last, classical conditioning is successfully demonstrated. Our results indicate that IWO-based neuromorphic transistors are interesting for neuromorphic applications.     

Coherence resonance in globally coupled neuronal networks with different neuron numbers

Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal networks with different neuron numbers. We confirm that for all neuronal networks with different neuron numbers there exist the array enhanced coherence resonance and the optimal synaptic conductance to cause the maximal spiking coherence. Furthermoremore, the enhancement effects of coupling on spiking coherence and on optimal synaptic conductance are almost the same, regardless of the neuron numbers in the neuronal networks. Therefore for all the neuronal networks with different neuron numbers in the brain, relative weak synaptic conductance (0.1 mS/cm²) is sufficient to induce the maximal spiking coherence and the best sub-threshold signal encoding.     

Implementation of synaptic learning rules by TaO_x memristors embedded with silver nanoparticles

As an alternative device for neuromorphic computing to conquer von Neumann bottleneck,the memristor serving as an artificial synapse has attracted much attention.The TaO_x memristors embedded with silver nanoparticles(Ag NPs)have been fabricated to implement synaptic plasticity and to investigate the effects of Ag NPs.The TaO_x memristors with and without Ag NPs are capable of simulating synaptic plasticity(PTP,STDP,and STP to LTP),learning,and memory behaviors.The conduction of the high resistance state(HRS) is driven by Schottky-emission mechanism.The embedment of Ag NPs causes the low resistance state(LRS) conduction governed by a Poole-Frenkel emission mechanism instead of a space-charge-limited conduction(SCLC) in a pure TaO_x system,which is ascribed to the Ag NPs enhancing electric field to produce additional traps and to reduce Coulomb potential energy of bound electrons to assist electron transport.Consequently,the enhanced electric fields induced by Ag NPs increase the learning strength and learning speed of the synapses.Additionally,they also improve synaptic sensitivity to stimuli.The linearity of conductance modulation and the reproducibility of conductance are improved as well.     

Fabrication and investigation of ferroelectric memristors with various synaptic plasticities

In the post-Moore era, neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks. Memristors have been proposed as a key part of neuromorphic computing architectures, and can be used to emulate the synaptic plasticities of the human brain. Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage, low write/read latency and tunable conductive states. However, among the reported ferroelectric memristors, the mechanisms of resistive switching are still under debate. In addition, there needs to be more research on emulation of the brain synapses using ferroelectric memristors. Herein, Cu/PbZr_0.52Ti_0.48O₃ (PZT)/Pt ferroelectric memristors have been fabricated. The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior. The synaptic plasticities, including excitatory post-synaptic current, paired-pulse facilitation, paired-pulse depression and spike time-dependent plasticity, have been mimicked by the PZT devices. Furthermore, the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models. This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.     

Model architecture for associative memory in a neural network of spiking neurons

A synaptic connectivity model is assembled on a spiking neuron network aiming to build up a dynamic pattern recognition system. The connection architecture includes gap junctions and both inhibitory and excitatory chemical synapses based on Hebb’s hypothesis. The network evolution resulting from external stimulus is sampled in a properly defined frequency space. Neurons’ responses to different current injections are mapped onto a subspace using Principal Component Analysis. Departing from the base attractor, related to a quiescent state, different external stimuli drive the network to different fixed points through specific trajectories in this subspace.     

Numerical study of the spiking instability caused by modulation frequency detuning in an actively mode-locked solid-state laser

We have studied both analytically and numerically the transient active mode locking in the case of a modulation frequency detuning. Analytical results for pulse width and position, extending previous works, are obtained within the frame of the stationary theory by assuming that a short pulse restores its shape after each round trip. Numerical evaluation leads to relaxation oscillations and change of the short-pulse shape. The results of both approaches agree with each other only for small detunings, when laser is operated in the steady state. For larger detunings the laser dynamics change dramatically, and the three operation modes observed earlier in experiments were demonstrated numerically: sequences of damped relaxation oscillations, deep periodic spiking and chaotic spiking. The periodic spiking can be used to increase the intensity of mode-locked pulses.