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A new photosensitive neuron model and its dynamics

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Frontiers of Information Technology & Electronic Engineering 2020年第9期21卷 1387-1396页

作者： Yong LIU Wan-jiang XU Jun MA Faris ALZAHRANI Aatef HOBINY School of Mathematics and Statistics Yancheng Teachers UniversityYancheng 224002China Department of Physics Lanzhou University of TechnologyLanzhou 730050China School of Science Chongqing University of Posts and TelecommunicationsChongqing 430065China NAAM-Research Group Department of MathematicsKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Biological neurons can receive inputs and capture a variety of external stimuli,which can be encoded and transmitted as different electric ***,the membrane potential is adjusted to activate the appropriate firing ***,reliable neuron models should take intrinsic biophysical effects and functional encoding into *** fascinating and important question is the physical mechanism for the transcription of external *** signals can be transmitted as a transmembrane current or a signal voltage for generating action *** present a photosensitive neuron model to estimate the nonlinear encoding and responses of neurons driven by external optical *** the model,a photocell(phototube)is used to activate a simple FitzHugh-Nagumo(FHN)neuron,and then external optical signals(illumination)are imposed to excite the photocell for generating a time-varying current/voltage *** photocell-coupled FHN neuron can therefore capture and encode external optical signals,similar to artificial *** also present detailed bifurcation analysis for estimating the mode transition and firing pattern selection of neuronal electrical *** sampled time series can reproduce the main characteristics of biological neurons(quiescent,spiking,bursting,and even chaotic behaviors)by activating the photocell in the neural *** results could be helpful in giving possible guidance for studying neurodynamics and applying neural circuits to detect optical signals.

关键词： Photosensitive neuron neuron model Bifurcation Bursting Photocell

Memristive neuron model with an adapting synapse and its hardware experiments

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Science China(Technological Sciences) 2021年第5期64卷 1107-1117页

作者： BAO BoCheng ZHU YongXin MA Jun BAO Han WU HuaGan CHEN Mo School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China Department of Physics Lanzhou University of TechnologyLanzhou 730050China

Electromagnetic induction effect caused by neuron potential can be mimicked using *** paper considers a fluxcontrolled memristor to imitate the electromagnetic induction effect of adapting feedback synapse and presents a memristive neuron model with the adapting *** memristive neuron model is three-dimensional and *** has the time-varying equilibria with multiple stabilities,which results in the global coexistence of multiple firing *** numerical plots are executed to uncover diverse coexisting firing patterns in the memristive neuron ***,a nonlinear fitting scheme is raised and a fitting activation function circuit is employed to implement the memristive mono-neuron *** coexisting firing patterns are observed from the hardware experiment circuit and the measured results verify the numerical simulations well.

关键词： memristor neuron model coexisting firing patterns nonlinear fitting scheme hardware experiment

A neuron model with trainable activation function (TAF) and its MFNN supervised learning

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Science in China(Series F) 2001年第5期44卷 366-375页

作者：吴佑寿赵明生 Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

This paper addresses a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived from a primitive neuron activation function by training. The BP like learning al-gorithm has been presented for MFNN constructed by neurons of TAP model. Several simulation ex-amples are given to show the network capacity and performance advantages of the new MFNN in com-parison with that of conventional sigmoid MFNN.

关键词： neuron model neural network TAF neuron model learning algorithm.

Method of Adaptive neuron model Utilizing Information of Local Samples For Loss Prediction in the Road Traffic Accident

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Method of Adaptive Neuron Model Utilizing Information of Loc...

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

第一届交通运输工程国际学术会议

作者： ZHAI Jun, YANG Xiaojia, SHEN Lixin, and CHEN Yan School of Economics & Management, Dalian Maritime University, P.R.China, 116026

This paper presents an adaptive neuron model utilizing information of local samples-the AUILS neuron model. Differing from traditional neuron models, the AUILS neuron model fully employs the experience samples information within the local range and well embodies the association and analogy functions of cerebrum. The neural network, which is simple in structure and fast in learning speed, can realize the nonlinear mapping relationship between multi-input and multi-output. Through investigating the properties of AUILS, we build a method based on the neuron model for loss prediction in the road traffic accident, which takes full advantage of expert experiences to estimate the loss value according to the accident number, deaths and wounds. It is verified that using the AUILS model, expert experiences can be well expressed in the prediction results.

关键词： neuron model neural networks road traffic accidents loss prediction

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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 *** to the working mechanism of human brain,the SNN system transmits information through the spiking action of ***,artificial neurons are critical building blocks for constructing SNN in *** are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological *** have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional *** 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 *** this review,the recent reports of artificial neurons based on memristive devices are *** addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic ***,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

The effect of oxygen concentration on the coupled neurons:Rich spiking patterns and synchronization

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Science China(Technological Sciences) 2020年第11期63卷 2339-2348页

作者： HE ZhiWei YAO ChengGui Department of Mathematics Shaoxing UniversityShaoxing 312000China College of Mathematics Physics and Information EngineeringJiaxing UniversityJiaxing 314000China

The dynamical microenvironments play a crucial role in neuronal spiking *** this paper,we investigated the effect of oxygen concentration on different synchronous spiking patterns of two coupled neuron models by including dynamical ion *** coupling modes of electrical diffusive coupling and potassium diffusive coupling were *** these two cases,oxygen concentration exhibited an important role in the synchronous spiking patterns between two coupled neurons,and extremely rich electrical activities were *** the potassium diffusive coupling,differential synchronous patterns of oscillation state(OS),synchronous epileptic seizure state(SSZ)and synchronous spreading depression state(SSD)as well as SZ and SD bursting states were *** the electrical diffusive coupling,differential synchronous patterns of resting state(RS),SSZ and SSD were observed.

关键词： neuron model phase synchronization electrical diffusive coupling potassium diffusive coupling

A novel type of neural networks for feature engineering of geological data:Case studies of coal and gas hydrate-bearing sediments

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Geoscience Frontiers 2020年第5期11卷 1511-1531页

作者： Lishuai Jiang Yang Zhao Naser Golsanami Lianjun Chen Weichao Yan State Key Laboratory of Mining Disaster Prevention and Control Shandong University of Science and TechnologyQingdao 266590China College of Energy and Mining Engineering Shandong University of Science and TechnologyQingdao 266590China College of Safety and Environmental Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Well Logging School of GeosciencesChina University of Petroleum(East China)Qingdao 266580China

The nature of the measured data varies among different disciplines of *** rock engineering,features of data play a leading role in determining the feasible methods of its proper *** present study focuses on resolving one of the major deficiencies of conventional neural networks(NNs)in dealing with rock engineering ***,since the samples are obtained from hundreds of meters below the surface with the utmost difficulty,the number of samples is always ***,the experimental analysis of these samples may result in many repetitive values and 0 ***,conventional neural networks are incapable of making robust models in the presence of such *** the other hand,these networks strongly depend on the initial weights and bias values for making reliable *** this in mind,the current research introduces a novel kind of neural network processing framework for the geological that does not suffer from the limitations of the conventional *** introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other *** method,being completely different from the conventional NNs,re-arranges all the basic elements of the neuron model into a new ***,its mathematical calculations were performed from the very ***,the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time *** new kind of network was first evaluated through computer-based simulations of rock cracks in the 3 DEC *** the model’s reliability was confirmed,it was adopted in two case studies for estimating respectively tensile strength and shear strength of real rock *** samples were coal core samples from the Southern Qinshui Basin of China,and gas hydrate-bearing sediment(GHBS)samples from the Nankai Trough of *** coa

关键词： Tensile strength Shear strength Gas Hydrate Feature engineering Rock engineering data neuron model