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Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

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Chinese Physics B 2024年第4期33卷 729-734页

作者：鲁婕妤谢小华卢亚平吴亚联李春来马铭磷 School of Automation and Electronic Information Xiangtan UniversityXiangtan 411105China School of Computer Science School of Cyberspace Science Xiangtan UniversityXiangtan 411105China

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

关键词： small-world networks Rulkov neurons memristor synchronization

Advances in memristor based artificial neuron fabrication-materials,models,and applications

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International Journal of Extreme Manufacturing 2024年第1期6卷 27-50页

作者： Jingyao Bian Zhiyong Liu Ye Tao Zhongqiang Wang Xiaoning Zhao Ya Lin Haiyang Xu Yichun Liu Key Laboratory for UV Light-Emitting Materials and Technology(Northeast Normal University) Ministry of Education5268 Renmin StreetChangchunPeople’s Republic of China

Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

关键词： artificial neuron memristor memristive materials neuron model micro-nano manufacturing spiking neural network

Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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Science China Technological Sciences 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou University School of Computer Science and Technology Harbin Institute of Technology

Inspired by basic circuit connection methods, memristors can also be utilized in the construction of complex discrete chaotic systems. To investigate the dynamical effects of hybrid memristors, we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations, respectively. Taking the HTMH mapping structure with hybrid parallel/cascade operation as an example, this map possesses a spatial invariant set whose stability is closely related to the initial states of the memristors. Dynamics distributions and bifurcation behaviours dependent on the control parameters are explored with numerical tools. Specifically, the memristor initial offset-boosting mechanism is theoretically demonstrated, and memristor initial offset-boosting behaviours are numerically verified. The results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite attractors. In addition, an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high randomness. Notably, the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs) to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

One memristor–one electrolyte-gated transistor-based high energy-efficient dropout neuronal units

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Chinese Physics B 2024年第6期33卷 569-573页

作者：李亚霖时凯璐朱一新方晓崔航源万青万昌锦 School of Electronic Science&Engineering and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China Yongjiang Laboratory(Y-LAB) Ningbo 315202China

Artificial neural networks(ANN) have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit(1R1T-DNU) based on one memristor–one electrolyte-gated transistor with an ultralow energy consumption of 25 p J/spike. A dropout neural network is constructed based on such a device and has been verified by MNIST dataset, demonstrating high recognition accuracies(> 90%) within a large range of dropout probabilities up to40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.

关键词： dropout neuronal unit synaptic transistors memristor artificial neural network

Optically driven intelligent computing with ZnO memristor

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Fundamental Research 2024年第1期4卷 158-166页

作者： Jing Yang Lingxiang Hu Liufeng Shen Jingrui Wang Peihong Cheng Huanming Lu Fei Zhuge Zhizhen Ye Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100029China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China

Artificial vision is crucial for most artificial intelligence applications.Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors,memories,and processors,which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy.Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory(ISCM)are key candidates for solving such challenges and therefore attract increasing attention.At present,the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices.Here,we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere.This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation,owing to its ability to reversibly tune the memconductance with light.Moreover,the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism.Hence,this study is an important step towards the next generation of artificial visual systems.

关键词： memristor All-optically controlling ZnO thin filmArtificial vision Nonvolatile neuromorphic computing Logic-in-memory

Application of artificial synapse based on all-inorganic perovskite memristor in neuromorphic computing

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Nano Materials Science 2024年第1期6卷 68-76页

作者： Fang Luo Wen-Min Zhong Xin-Gui Tang Jia-Ying Chen Yan-Ping Jiang Qiu-Xiang Liu School of Physics&Optoelectric Engineering Guangdong University of TechnologyGuangzhou Higher Education Mega CenterGuangzhou510006China

Artificial synapse inspired by the biological brain has great potential in the field of neuromorphic computing and artificial intelligence.The memristor is an ideal artificial synaptic device with fast operation and good tolerance.Here,we have prepared a memristor device with Au/CsPbBr_(3)/ITO structure.The memristor device exhibits resistance switching behavior,the high and low resistance states no obvious decline after 400 switching times.The memristor device is stimulated by voltage pulses to simulate biological synaptic plasticity,such as long-term potentiation,long-term depression,pair-pulse facilitation,short-term depression,and short-term potentiation.The transformation from short-term memory to long-term memory is achieved by changing the stimulation frequency.In addition,a convolutional neural network was constructed to train/recognize MNIST handwritten data sets;a distinguished recognition accuracy of~96.7%on the digital image was obtained in 100 epochs,which is more accurate than other memristor-based neural networks.These results show that the memristor device based on CsPbBr3 has immense potential in the neuromorphic computing system.

关键词： memristor CsPbBr_(3) Resistive switching Artificial synapse Neuromorphic computing

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Electron push-pull effects induced performance promotion in covalent organic polymer thin films-based memristor for neuromorphic application

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Chinese Chemical Letters 2024年第5期35卷 227-231页

作者： Panke Zhou Hong Yu Mun Yin Chee Tao Zeng Tianli Jin Hongling Yu Shuo Wu Wen Siang Lew Xiong Chen State Key Laboratory of Photocatalysis on Energy and Environment College of ChemistryFuzhou UniversityFuzhou 350116China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore

Covalent organic polymer(COP)thin film-based memristors have generated intensive research interest,but the studies are still in their infancy.Herein,by controlling the content of hydroxyl groups in the aldehyde monomer,Py-COP thin films with different electronic push-pull effects were fabricated bearing distinct memory performances,where the films were prepared by the solid-liquid interface method on the ITO substrates and further fabricated as memory devices with ITO/Py-COPs/Ag architectures.The Py-COP-1-based memory device only exhibited binary memory behavior with an ON/OFF ratio of 1:10^(1.87).In contrast,the device based on Py-COP-2 demonstrated ternary memory behavior with an ON/OFF ratio of 1:10^(0.6):10^(3.1) and a ternary yield of 55%.The ternary memory mechanism of the ITO/Py-COP-2/Ag memory device is most likely due to the combination of the trapping of charge carriers and conductive filaments.Interestingly,the Py-COPs-based devices can successfully emulate the synaptic potentiation/depression behavior,clarifying the programmability of these devices in neuromorphic systems.These results suggest that the electronic properties of COPs can be precisely tuned at the molecular level,which provides a promising route for designing multi-level memory devices.

关键词： Covalent organic polymers Push-pull effects Multi-level memory memristor

Artificial Intelligence Meets Flexible Sensors:Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

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Nano-Micro Letters 2024年第1期16卷 235-273页

作者： Tianming Sun Bin Feng Jinpeng Huo Yu Xiao Wengan Wang Jin Peng Zehua Li Chengjie Du Wenxian Wang Guisheng Zou Lei Liu Department of Mechanical Engineering State Key Laboratory of Tribology in Advanced EquipmentKey Laboratory for Advanced Manufacturing by Materials Processing TechnologyMinistry of Education of PR ChinaTsinghua UniversityBeijing 100084People’s Republic of China College of Materials Science and Engineering Shanxi ProvinceTaiyuan University of TechnologyTaiyuan 030024People’s Republic of China

The recent wave of the artificial intelligence(AI)revolution has aroused unprecedented interest in the intelligentialize of human society.As an essential component that bridges the physical world and digital signals,flexible sensors are evolving from a single sensing element to a smarter system,which is capable of highly efficient acquisition,analysis,and even perception of vast,multifaceted data.While challenging from a manual perspective,the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm(machine learning)and the framework(artificial synapses)level.This review presents the recent progress of the emerging AI-driven,intelligent flexible sensing systems.The basic concept of machine learning and artificial synapses are introduced.The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed,which significantly advances the applications such as flexible sensory systems,soft/humanoid robotics,and human activity monitoring.As two of the most profound innovations in the twenty-first century,the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

关键词： Flexible electronics Wearable electronics Neuromorphic memristor Deep learning

Memristive oscillator to memristive map,energy characteristic

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Science China(Technological Sciences) 2024年第5期67卷 1567-1578页

作者： GUO YiTong MA Jun ZHANG XiaoFeng HU XiKui College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou730050China Department of Physics Lanzhou University of TechnologyLanzhou730050China School of Science Chongqing University of Posts and TelecommunicationsChongqing400065China

Most of nonlinear oscillators composed of capacitive and inductive variables can obtain the Hamilton energy by using the Helmholtz theorem when the models are rewritten in equivalent vector forms.The energy functions for biophysical neurons can be obtained by applying scale transformation on the physical field energy in their equivalent neural circuits.Realistic dynamical systems often have exact energy functions,while some mathematical models just suggest generic Lyapunov functions,and the energy function is effective to predict mode transition.In this paper,a memristive oscillator is approached by two kinds of memristor-based nonlinear circuits,and the energy functions are defined to predict the dependence of oscillatory modes on energy level.In absence of capacitive variable for capacitor,the physical time t and charge q are converted into dimensionless variables by using combination of resistance and inductance(L,R),e.g.,τ=t×R/L.Discrete energy function for each memristive map is proposed by applying the similar weights as energy function for the memristive oscillator.For example,energy function for the map is obtained by replacing the variables and parameters of the memristive oscillator with corresponding variables and parameters for the memristive map.The memristive map prefers to keep lower average energy than the memristive oscillator,and chaos is generated in a discrete system with two variables.The scheme is helpful for energy definition in maps,and it provides possible guidance for verifying the reliability of maps by considering the energy characteristic.

关键词： Hamilton energy memristor Helmholtz theorem scale transformation memristive circuit