Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation...Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse,are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh-Rose neuron model.Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns.The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors.Finally,the simulation circuit and DSP hardware implementation results validate the physical mechanism,as well as the reliability of the biological neuron model.展开更多
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 l...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.展开更多
In this paper, we investigate the dynamics in a class of discrete-time neuron mod-els. The neuron model we discussed, defined by such periodic input-output mapping as a sinusoidal function, has a remarkably larger mem...In this paper, we investigate the dynamics in a class of discrete-time neuron mod-els. The neuron model we discussed, defined by such periodic input-output mapping as a sinusoidal function, has a remarkably larger memory capacity than the conven-tional association system with the monotonous function. Our results show that the orbit of the model takes a conventional bifurcation route, from stable equilibrium, to periodicity, even to chaotic region. And the theoretical analysis is verified by numerical simula...展开更多
We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numeri...We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numerical solution using a recently proposed L1 predictor–corrector method.The given method is based on the L1-type discretization algorithm and the spline interpolation scheme.We perform the error and stability analyses for the given method.We perform graphical simulations demonstrating that the proposed FHN neuron model generates rich electrical activities of periodic spiking patterns,chaotic patterns,and quasi-periodic patterns.The motivation behind proposing a fractional-order improved FHN neuron model is that such a system can provide a more nuanced description of the process with better understanding and simulation of the neuronal responses by incorporating memory effects and non-local dynamics,which are inherent to many biological systems.展开更多
We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks(PINNs).The proposed method,splitting PINN,effectively add...We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks(PINNs).The proposed method,splitting PINN,effectively addresses the challenge of applying PINNs to forward dynamical systems and demonstrates improved accuracy through its application to neuron models.Specifically,we apply operator splitting to decompose the original neuron model into sub-problems that are then solved using PINNs.Moreover,we develop an L^(1) scheme for discretizing fractional derivatives in fractional neuron models,leading to improved accuracy and efficiency.The results of this study highlight the potential of splitting PINNs in solving both integer-and fractional-order neuron models,as well as other similar systems in computational science and engineering.展开更多
Objective The relationship between compressed dorsal root ganglion （DRG） neurons and firing pattern and sensitivity of neurons was studied in chronically the Hindmarsh-Rose （HR） neuronal model. Methods Spontane- o...Objective The relationship between compressed dorsal root ganglion （DRG） neurons and firing pattern and sensitivity of neurons was studied in chronically the Hindmarsh-Rose （HR） neuronal model. Methods Spontane- ous activities from single fibers of chronically compressed DRG neurons in rats were recorded, and divided into periodic and non-periodic firing patterns. The sensitivity of the two kinds of firing pattern neuron to sympathetic stimulation （SS） was compared. Result It was found that 27.3% of periodic firing neurons and 93.2% of non-periodic firing neurons responded to SS respectively （ periodic vs non-periodic, P 〈 0.01 ）. The responses to SS with different stimulation time were greater non-periodic firing neurons than periodic firing neurons （P 〈 0.01 ）. The non-periodic firing neurons obviously responded to SS. After the firing pattern of these neurons transformed to periodic firing pattern, their responses to SS disappeared or decreased obviously. The HR neuronal model exhibited a significantly greater response to perturbation in non-periodic （chaotic） firing pattern than in periodic firing pattern. Conelusion The non-periodic firing neurons with deterministic chaos are more sensitive to external stimuli than the periodic firing neurons.展开更多
In the presence of Gaussian white noise,we study the properties of voltage-controlled oscillator neuronmodel and discuss the effects of the additive and multiplicative noise.It is found that the additive noise can acc...In the presence of Gaussian white noise,we study the properties of voltage-controlled oscillator neuronmodel and discuss the effects of the additive and multiplicative noise.It is found that the additive noise can accelerate andcounterwork the firing of neuron,which depends on the value of central frequency of neuron itself,while multiplicativenoise can induce the continuous change or mutation of membrane potential.展开更多
Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms.The electrophysiological environment inside and outside of the nerve cell is different....Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms.The electrophysiological environment inside and outside of the nerve cell is different.Due to the continuous and periodical properties of electromagnetic fields in the cell during its operation,electronic components involving two capacitors and a memristor are effective in mimicking these physical features.In this paper,a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance.It is found that the memristive neural circuit can present abundant firing patterns without stimulus.The Hamilton energy function is deduced using the Helmholtz theorem.Further,a neuronal network consisting of memristive neurons is proposed by introducing energy coupling.The controllability and flexibility of parameters give the model the ability to describe the dynamics and synchronization behavior of the system.展开更多
The firing of a neuron model is mainly affected by the following factors:the magnetic field,external forcing current,time delay,etc.In this paper,a new time-delayed electromagnetic field coupled dual Hindmarsh-Rose ne...The firing of a neuron model is mainly affected by the following factors:the magnetic field,external forcing current,time delay,etc.In this paper,a new time-delayed electromagnetic field coupled dual Hindmarsh-Rose neuron network model is constructed.A magnetically controlled threshold memristor is improved to represent the self-connected and the coupled magnetic fields triggered by the dynamic change of neuronal membrane potential for the adjacent neurons.Numerical simulation confirms that the coupled magnetic field can activate resting neurons to generate rich firing patterns,such as spiking firings,bursting firings,and chaotic firings,and enable neurons to generate larger firing amplitudes.The study also found that the strength of magnetic coupling in the neural network also affects the number of peaks in the discharge of bursting firing.Based on the existing medical treatment background of mental illness,the effects of time lag in the coupling process against neuron firing are studied.The results confirm that the neurons can respond well to external stimuli and coupled magnetic field with appropriate time delay,and keep periodic firing under a wide range of external forcing current.展开更多
The ability to use induced pluripotent stem cells（i PSC）to model brain diseases is a powerful tool for unraveling mechanistic alterations in these disorders.Rodent models of brain diseases have spurred understanding...The ability to use induced pluripotent stem cells（i PSC）to model brain diseases is a powerful tool for unraveling mechanistic alterations in these disorders.Rodent models of brain diseases have spurred understanding of pathology but the concern arises that they may not recapitulate the full spectrum of neuron disruptions associated with human neuropathology.iPSC derived neurons,or other neural cell types,provide the ability to access pathology in cells derived directly from a patient＇s blood sample or skin biopsy where availability of brain tissue is limiting.Thus,utilization of iPSC to study brain diseases provides an unlimited resource for disease modelling but may also be used for drug screening for effective therapies and may potentially be used to regenerate aged or damaged cells in the future.Many brain diseases across the spectrum of neurodevelopment,neurodegenerative and neuropsychiatric are being approached by iPSC models.The goal of an iPSC based disease model is to identify a cellular phenotype that discriminates the disease-bearing cells from the control cells.In this mini-review,the importance of iPSC cell models validated for pluripotency,germline competency and function assessments is discussed.Selected examples for the variety of brain diseases that are being approached by iPSC technology to discover or establish the molecular basis of the neuropathology are discussed.展开更多
We present a novel in vitro model in which to investigate the efficacy of experimental drugs for the promotion of axon regeneration in the central nervous system. We co-cultured rat hippocampal neurons and cerebral co...We present a novel in vitro model in which to investigate the efficacy of experimental drugs for the promotion of axon regeneration in the central nervous system. We co-cultured rat hippocampal neurons and cerebral cortical oligodendrocytes, and tested the co-culture system using a Nogo-66 receptor antagonist peptide（NEP1–40）, which promotes axonal growth. Primary cultured oligodendrocytes suppressed axonal growth in the rat hippocampus, but NEP1–40 stimulated axonal growth in the co-culture system. Our results confirm the validity of the neuron-oligodendrocyte co-culture system as an assay for the evaluation of drugs for axon regeneration in the central nervous system.展开更多
How do individual neurons develop and how are they in- tegrated into neuronal circuitry？ To answer this question is essential to understand how the nervous system develops and how it is maintained during the adult li...How do individual neurons develop and how are they in- tegrated into neuronal circuitry？ To answer this question is essential to understand how the nervous system develops and how it is maintained during the adult life. A neural stem cell must go through several stages of maturation, including proliferation, migration, differentiation, and integration, to become fully embedded to an existing neuronal circuit. The knowledge on this topic so far has come mainly from cell culture studies. Studying the development of individual neurons within intact neuronal networks in vivo is inherently difficult. Most neurons are generated form neural stem cells during embryonic and early postnatal development.展开更多
We investigate transcranial magneto–acoustical stimulation（TMAS） for noninvasive brain neuromodulation in vivo.TMAS as a novel technique uses an ultrasound wave to induce an electric current in the brain tissue in ...We investigate transcranial magneto–acoustical stimulation（TMAS） for noninvasive brain neuromodulation in vivo.TMAS as a novel technique uses an ultrasound wave to induce an electric current in the brain tissue in the static magnetic field. It has the advantage of high spatial resolution and penetration depth. The mechanism of TMAS onto a neuron is analyzed by combining the TMAS principle and Hodgkin–Huxley neuron model. The anesthetized rats are stimulated by TMAS, resulting in the local field potentials which are recorded and analyzed. The simulation results show that TMAS can induce neuronal action potential. The experimental results indicate that TMAS can not only increase the amplitude of local field potentials but also enhance the effect of focused ultrasound stimulation on the neuromodulation. In summary, TMAS can accomplish brain neuromodulation, suggesting a potentially powerful noninvasive stimulation method to interfere with brain rhythms for diagnostic and therapeutic purposes.展开更多
The nature of the measured data varies among different disciplines of geosciences.In rock engineering,features of data play a leading role in determining the feasible methods of its proper manipulation.The present stu...The nature of the measured data varies among different disciplines of geosciences.In rock engineering,features of data play a leading role in determining the feasible methods of its proper manipulation.The present study focuses on resolving one of the major deficiencies of conventional neural networks(NNs)in dealing with rock engineering data.Herein,since the samples are obtained from hundreds of meters below the surface with the utmost difficulty,the number of samples is always limited.Meanwhile,the experimental analysis of these samples may result in many repetitive values and 0 s.However,conventional neural networks are incapable of making robust models in the presence of such data.On the other hand,these networks strongly depend on the initial weights and bias values for making reliable predictions.With 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 NNs.The introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other points.This method,being completely different from the conventional NNs,re-arranges all the basic elements of the neuron model into a new structure.Therefore,its mathematical calculations were performed from the very beginning.Moreover,the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time being.This new kind of network was first evaluated through computer-based simulations of rock cracks in the 3 DEC environment.After 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.These samples were coal core samples from the Southern Qinshui Basin of China,and gas hydrate-bearing sediment(GHBS)samples from the Nankai Trough of Japan.The coal samples used in the experiments underwent nuclear magnetic resonance(NMR)measurements,and Scanning Electron Microscopy(SEM)imaging to investigate their original micro and macro fractures.Once done with these experiments,measurement of the rock mechanical properties,including tensile strength,was performed using a rock mechanical test system.However,the shear strength of GHBS samples was acquired through triaxial and direct shear tests.According to the obtained result,the new network structure outperformed the conventional neural networks in both cases of simulation-based and case study estimations of the tensile and shear strength.Even though the proposed approach of the current study originally aimed at resolving the issue of having a limited dataset,its unique properties would also be applied to larger datasets from other subsurface measurements.展开更多
Fohlmeister-Coleman-Miller model of retinal ganglion cells consists of five ion channels; these are sodium channels, calcium channels, and 3 types of potassium channels. An increasing number of studies have investigat...Fohlmeister-Coleman-Miller model of retinal ganglion cells consists of five ion channels; these are sodium channels, calcium channels, and 3 types of potassium channels. An increasing number of studies have investigated sodium channels, voltage-gated potassium channels, and delayed rectifier potassium channels. However, little is known about calcium channels, and in particular the dynamics and computational models of calcium ions. Retinal prostheses have been designed to assist with sight recovery for the blind, and in the present study, the effects of calcium ions in retinal ganglion cell models were analyzed with regard to calcium channel potential and calcium-activated potassium potential. Using MATLAB software, calcium conductance and calcium current from the Fohlmeister-Coleman-Miller model, under clamped voltages, were numerically computed using backward Euler methods. Subsequently, the Fohlmeister-Coleman-Miller model was simulated with the absence of calcium-current （lca） or calcium-activated potassium current （lK, Ca）. The model was also analyzed according to the phase plane method. The relationship curve between peak calcium current and clamped potentials revealed an inverted bell shape, and the calcium-activated potassium current increased the frequency of firing and the peak of membrane potential. Results suggested that calcium ion concentrations play an important role in controlling the peak and the magnitude of peak membrane voltage in retinal ganglion cells.展开更多
Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer＇s disease （AD）...Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer＇s disease （AD） can lead to loss of functional synapses in the central nervous system, and associative memory functions in patients with AD are often impaired, but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region. In this study, based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region, a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances： normal, synaptic deletion and synaptic compensation, according to Ruppin＇s synaptic deletion and compensation theory. The influences of AD on hetero-associative memory were further analyzed. The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions. With increasing synaptic deletion level, both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased. With gradual increasing synaptic compensation, the associative memory functions of the network were improved and the mean firing rates were increased. The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region. Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region, and can also result in memory dysfunction. To some extent, synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.展开更多
In this work, we specify potential elements of the brain to sense and regulate the energy metabolism of the organism. Our numerical investigations base on neurochemical experiments demonstrating a biphasic association...In this work, we specify potential elements of the brain to sense and regulate the energy metabolism of the organism. Our numerical investigations base on neurochemical experiments demonstrating a biphasic association between brain glucose level and neuronal activity. The dynamics of high and low affine KATP channels are most likely to play a decisive role in neuronal activity. We develop a coupled Hodgkin-Huxley model describing the interactive behavior of inhibitory GABAergic and excitatory dopaminergic neurons projecting into the caudate nucleus. The novelty in our approach is that we include the synaptic coupling of GABAergic and dopaminergic neurons as well as the interaction of high and low affine KATP channels. Both are crucial mechanisms described by kinetic models. Simulations demonstrate that our new model is coherent with neurochemical in vitro experiments. Even experimental interventions with glibenclamide and glucosamine are reproduced by our new model. Our results show that the considered dynamics of high and low affine KATP channels may be a driving force in energy sensing and global regulation of the energy metabolism, which supports central aspects of the new Selfish Brain Theory. Moreover, our simulations suggest that firing frequencies and patterns of GABAergic and dopaminergic neurons are correlated to their neurochemical outflow.展开更多
This paper is concerned with bifurcations and chaos control of the Hindmarsh-Rose(HR)neuronal model with the time-delayed feedback control.By stability and bifurcation analysis,we find that the excitable neuron can em...This paper is concerned with bifurcations and chaos control of the Hindmarsh-Rose(HR)neuronal model with the time-delayed feedback control.By stability and bifurcation analysis,we find that the excitable neuron can emit spikes via the subcritical Hopf bifurcation,and exhibits periodic or chaotic spiking/bursting behaviors with the increase of external current.For the purpose of control of chaos,we adopt the time-delayed feedback control,and convert chaos control to the Hopf bifurcation of the delayed feedback system.Then the analytical conditions under which the Hopf bifurcation occurs are given with an explicit formula.Based on this,we show the Hopf bifurcation curves in the two-parameter plane.Finally,some numerical simulations are carried out to support the theoretical results.It is shown that by appropriate choice of feedback gain and time delay,the chaotic orbit can be controlled to be stable.The adopted method in this paper is general and can be applied to other neuronal models.It may help us better understand the bifurcation mechanisms of neural behaviors.展开更多
Biological neurons can receive inputs and capture a variety of external stimuli,which can be encoded and transmitted as different electric signals.Thus,the membrane potential is adjusted to activate the appropriate fi...Biological neurons can receive inputs and capture a variety of external stimuli,which can be encoded and transmitted as different electric signals.Thus,the membrane potential is adjusted to activate the appropriate firing modes.Indeed,reliable neuron models should take intrinsic biophysical effects and functional encoding into consideration.One fascinating and important question is the physical mechanism for the transcription of external signals.External signals can be transmitted as a transmembrane current or a signal voltage for generating action potentials.We present a photosensitive neuron model to estimate the nonlinear encoding and responses of neurons driven by external optical signals.In 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 source.The photocell-coupled FHN neuron can therefore capture and encode external optical signals,similar to artificial eyes.We also present detailed bifurcation analysis for estimating the mode transition and firing pattern selection of neuronal electrical activities.The 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 circuit.These results could be helpful in giving possible guidance for studying neurodynamics and applying neural circuits to detect optical signals.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62061014)Technological Innovation Projects in the Field of Artificial Intelligence in Liaoning province(Grant No.2023JH26/10300011)Basic Scientific Research Projects in Department of Education of Liaoning Province(Grant No.JYTZD2023021).
文摘Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse,are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh-Rose neuron model.Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns.The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors.Finally,the simulation circuit and DSP hardware implementation results validate the physical mechanism,as well as the reliability of the biological neuron model.
基金supported financially by the fund from the Ministry of Science and Technology of China(Grant No.2019YFB2205100)the National Science Fund for Distinguished Young Scholars(No.52025022)+3 种基金the National Nature Science Foundation of China(Grant Nos.U19A2091,62004016,51732003,52072065,1197407252272140 and 52372137)the‘111’Project(Grant No.B13013)the Fundamental Research Funds for the Central Universities(Nos.2412023YQ004 and 2412022QD036)the funding from Jilin Province(Grant Nos.20210201062GX,20220502002GH,20230402072GH,20230101017JC and 20210509045RQ)。
文摘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.
基金Specialized research fund for outstanding young scholars in universities of Shanghai (GrantNo2-2008-26)
文摘In this paper, we investigate the dynamics in a class of discrete-time neuron mod-els. The neuron model we discussed, defined by such periodic input-output mapping as a sinusoidal function, has a remarkably larger memory capacity than the conven-tional association system with the monotonous function. Our results show that the orbit of the model takes a conventional bifurcation route, from stable equilibrium, to periodicity, even to chaotic region. And the theoretical analysis is verified by numerical simula...
文摘We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numerical solution using a recently proposed L1 predictor–corrector method.The given method is based on the L1-type discretization algorithm and the spline interpolation scheme.We perform the error and stability analyses for the given method.We perform graphical simulations demonstrating that the proposed FHN neuron model generates rich electrical activities of periodic spiking patterns,chaotic patterns,and quasi-periodic patterns.The motivation behind proposing a fractional-order improved FHN neuron model is that such a system can provide a more nuanced description of the process with better understanding and simulation of the neuronal responses by incorporating memory effects and non-local dynamics,which are inherent to many biological systems.
基金supported by AFOSR MURI funding(FA9550-20-1-0358)Graphs and Spikes for Earth and Embedded Systems(SEA-CROGS)project+2 种基金supported by the National Natural Science Foundation of China(12171283)the National Key R&D Program of China(2021YFA1000202,2021YFA1000200)the Science Foundation Program for Distinguished Young Scholars of Shandong(Overseas)(2022HWYQ-045).
文摘We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks(PINNs).The proposed method,splitting PINN,effectively addresses the challenge of applying PINNs to forward dynamical systems and demonstrates improved accuracy through its application to neuron models.Specifically,we apply operator splitting to decompose the original neuron model into sub-problems that are then solved using PINNs.Moreover,we develop an L^(1) scheme for discretizing fractional derivatives in fractional neuron models,leading to improved accuracy and efficiency.The results of this study highlight the potential of splitting PINNs in solving both integer-and fractional-order neuron models,as well as other similar systems in computational science and engineering.
基金This work was supported by the National Natural Science Foundation of China （30030040）.
文摘Objective The relationship between compressed dorsal root ganglion （DRG） neurons and firing pattern and sensitivity of neurons was studied in chronically the Hindmarsh-Rose （HR） neuronal model. Methods Spontane- ous activities from single fibers of chronically compressed DRG neurons in rats were recorded, and divided into periodic and non-periodic firing patterns. The sensitivity of the two kinds of firing pattern neuron to sympathetic stimulation （SS） was compared. Result It was found that 27.3% of periodic firing neurons and 93.2% of non-periodic firing neurons responded to SS respectively （ periodic vs non-periodic, P 〈 0.01 ）. The responses to SS with different stimulation time were greater non-periodic firing neurons than periodic firing neurons （P 〈 0.01 ）. The non-periodic firing neurons obviously responded to SS. After the firing pattern of these neurons transformed to periodic firing pattern, their responses to SS disappeared or decreased obviously. The HR neuronal model exhibited a significantly greater response to perturbation in non-periodic （chaotic） firing pattern than in periodic firing pattern. Conelusion The non-periodic firing neurons with deterministic chaos are more sensitive to external stimuli than the periodic firing neurons.
基金National Natural Science Foundation of China under Grant No.30600122Natural Science Foundation of Guangdong Province of China under Grant No.06025073the Natural Science Foundation of South China University of Technology under Grant No.B14-E5050200
文摘In the presence of Gaussian white noise,we study the properties of voltage-controlled oscillator neuronmodel and discuss the effects of the additive and multiplicative noise.It is found that the additive noise can accelerate andcounterwork the firing of neuron,which depends on the value of central frequency of neuron itself,while multiplicativenoise can induce the continuous change or mutation of membrane potential.
基金funded by the National Natural Science Foundation of China(Grant No.12302070)the Ningxia Science and Technology Leading Talent Training Program(Grant No.2022GKLRLX04)。
文摘Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms.The electrophysiological environment inside and outside of the nerve cell is different.Due to the continuous and periodical properties of electromagnetic fields in the cell during its operation,electronic components involving two capacitors and a memristor are effective in mimicking these physical features.In this paper,a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance.It is found that the memristive neural circuit can present abundant firing patterns without stimulus.The Hamilton energy function is deduced using the Helmholtz theorem.Further,a neuronal network consisting of memristive neurons is proposed by introducing energy coupling.The controllability and flexibility of parameters give the model the ability to describe the dynamics and synchronization behavior of the system.
基金Project supported by the National Natural Science Foundation of China(Grant No.61873186)。
文摘The firing of a neuron model is mainly affected by the following factors:the magnetic field,external forcing current,time delay,etc.In this paper,a new time-delayed electromagnetic field coupled dual Hindmarsh-Rose neuron network model is constructed.A magnetically controlled threshold memristor is improved to represent the self-connected and the coupled magnetic fields triggered by the dynamic change of neuronal membrane potential for the adjacent neurons.Numerical simulation confirms that the coupled magnetic field can activate resting neurons to generate rich firing patterns,such as spiking firings,bursting firings,and chaotic firings,and enable neurons to generate larger firing amplitudes.The study also found that the strength of magnetic coupling in the neural network also affects the number of peaks in the discharge of bursting firing.Based on the existing medical treatment background of mental illness,the effects of time lag in the coupling process against neuron firing are studied.The results confirm that the neurons can respond well to external stimuli and coupled magnetic field with appropriate time delay,and keep periodic firing under a wide range of external forcing current.
文摘The ability to use induced pluripotent stem cells（i PSC）to model brain diseases is a powerful tool for unraveling mechanistic alterations in these disorders.Rodent models of brain diseases have spurred understanding of pathology but the concern arises that they may not recapitulate the full spectrum of neuron disruptions associated with human neuropathology.iPSC derived neurons,or other neural cell types,provide the ability to access pathology in cells derived directly from a patient＇s blood sample or skin biopsy where availability of brain tissue is limiting.Thus,utilization of iPSC to study brain diseases provides an unlimited resource for disease modelling but may also be used for drug screening for effective therapies and may potentially be used to regenerate aged or damaged cells in the future.Many brain diseases across the spectrum of neurodevelopment,neurodegenerative and neuropsychiatric are being approached by iPSC models.The goal of an iPSC based disease model is to identify a cellular phenotype that discriminates the disease-bearing cells from the control cells.In this mini-review,the importance of iPSC cell models validated for pluripotency,germline competency and function assessments is discussed.Selected examples for the variety of brain diseases that are being approached by iPSC technology to discover or establish the molecular basis of the neuropathology are discussed.
基金supported by the Youth Program of the National Natural Science Foundation of China,No.11102235the Key Science and Technology Support Project of Tianjin City of China,No.14ZCZDGX00500+3 种基金the Key Program of the Natural Science Foundation of Tianjin City of China,No.12JCZDJC24100the Science and Technology Foundation of Health Bureau of Tianjin City of China,No.2013KZ134,2014KZ135the Youth Program of the Natural Science Foundation of Tianjin City of China,No.12JCQNJC07100the Seed Foundation of Affiliated Hospital of Logistics University of Chinese People’s Armed Police Force,No.FYM201432
文摘We present a novel in vitro model in which to investigate the efficacy of experimental drugs for the promotion of axon regeneration in the central nervous system. We co-cultured rat hippocampal neurons and cerebral cortical oligodendrocytes, and tested the co-culture system using a Nogo-66 receptor antagonist peptide（NEP1–40）, which promotes axonal growth. Primary cultured oligodendrocytes suppressed axonal growth in the rat hippocampus, but NEP1–40 stimulated axonal growth in the co-culture system. Our results confirm the validity of the neuron-oligodendrocyte co-culture system as an assay for the evaluation of drugs for axon regeneration in the central nervous system.
基金supported by DFG Schwerpunkt program 1392(project MA 4113/2-2)cluster of Excellence and DFG Research Center Nanoscale Microscopy and Molecular Physiology of the Brain(project B1-9)+1 种基金the German Ministry of Research and Education(BMBFproject 1364480)
文摘How do individual neurons develop and how are they in- tegrated into neuronal circuitry？ To answer this question is essential to understand how the nervous system develops and how it is maintained during the adult life. A neural stem cell must go through several stages of maturation, including proliferation, migration, differentiation, and integration, to become fully embedded to an existing neuronal circuit. The knowledge on this topic so far has come mainly from cell culture studies. Studying the development of individual neurons within intact neuronal networks in vivo is inherently difficult. Most neurons are generated form neural stem cells during embryonic and early postnatal development.
基金supported by the National Natural Science Foundation of China(Grant Nos.61503321 and 61273063)the Natural Science Foundation of Hebei Province,China(Grant No.F2014203161)
文摘We investigate transcranial magneto–acoustical stimulation（TMAS） for noninvasive brain neuromodulation in vivo.TMAS as a novel technique uses an ultrasound wave to induce an electric current in the brain tissue in the static magnetic field. It has the advantage of high spatial resolution and penetration depth. The mechanism of TMAS onto a neuron is analyzed by combining the TMAS principle and Hodgkin–Huxley neuron model. The anesthetized rats are stimulated by TMAS, resulting in the local field potentials which are recorded and analyzed. The simulation results show that TMAS can induce neuronal action potential. The experimental results indicate that TMAS can not only increase the amplitude of local field potentials but also enhance the effect of focused ultrasound stimulation on the neuromodulation. In summary, TMAS can accomplish brain neuromodulation, suggesting a potentially powerful noninvasive stimulation method to interfere with brain rhythms for diagnostic and therapeutic purposes.
文摘The nature of the measured data varies among different disciplines of geosciences.In rock engineering,features of data play a leading role in determining the feasible methods of its proper manipulation.The present study focuses on resolving one of the major deficiencies of conventional neural networks(NNs)in dealing with rock engineering data.Herein,since the samples are obtained from hundreds of meters below the surface with the utmost difficulty,the number of samples is always limited.Meanwhile,the experimental analysis of these samples may result in many repetitive values and 0 s.However,conventional neural networks are incapable of making robust models in the presence of such data.On the other hand,these networks strongly depend on the initial weights and bias values for making reliable predictions.With 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 NNs.The introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other points.This method,being completely different from the conventional NNs,re-arranges all the basic elements of the neuron model into a new structure.Therefore,its mathematical calculations were performed from the very beginning.Moreover,the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time being.This new kind of network was first evaluated through computer-based simulations of rock cracks in the 3 DEC environment.After 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.These samples were coal core samples from the Southern Qinshui Basin of China,and gas hydrate-bearing sediment(GHBS)samples from the Nankai Trough of Japan.The coal samples used in the experiments underwent nuclear magnetic resonance(NMR)measurements,and Scanning Electron Microscopy(SEM)imaging to investigate their original micro and macro fractures.Once done with these experiments,measurement of the rock mechanical properties,including tensile strength,was performed using a rock mechanical test system.However,the shear strength of GHBS samples was acquired through triaxial and direct shear tests.According to the obtained result,the new network structure outperformed the conventional neural networks in both cases of simulation-based and case study estimations of the tensile and shear strength.Even though the proposed approach of the current study originally aimed at resolving the issue of having a limited dataset,its unique properties would also be applied to larger datasets from other subsurface measurements.
基金the National Natural Science Foundation of China,No. 30870649Science and Technology 973 Project,No. 2005CB724302
文摘Fohlmeister-Coleman-Miller model of retinal ganglion cells consists of five ion channels; these are sodium channels, calcium channels, and 3 types of potassium channels. An increasing number of studies have investigated sodium channels, voltage-gated potassium channels, and delayed rectifier potassium channels. However, little is known about calcium channels, and in particular the dynamics and computational models of calcium ions. Retinal prostheses have been designed to assist with sight recovery for the blind, and in the present study, the effects of calcium ions in retinal ganglion cell models were analyzed with regard to calcium channel potential and calcium-activated potassium potential. Using MATLAB software, calcium conductance and calcium current from the Fohlmeister-Coleman-Miller model, under clamped voltages, were numerically computed using backward Euler methods. Subsequently, the Fohlmeister-Coleman-Miller model was simulated with the absence of calcium-current （lca） or calcium-activated potassium current （lK, Ca）. The model was also analyzed according to the phase plane method. The relationship curve between peak calcium current and clamped potentials revealed an inverted bell shape, and the calcium-activated potassium current increased the frequency of firing and the peak of membrane potential. Results suggested that calcium ion concentrations play an important role in controlling the peak and the magnitude of peak membrane voltage in retinal ganglion cells.
基金the National Natural Science Foundation of China,No.30870649the Natural Science Foundation of Tianjin,No.08JCYBJC03300
文摘Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer＇s disease （AD） can lead to loss of functional synapses in the central nervous system, and associative memory functions in patients with AD are often impaired, but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region. In this study, based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region, a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances： normal, synaptic deletion and synaptic compensation, according to Ruppin＇s synaptic deletion and compensation theory. The influences of AD on hetero-associative memory were further analyzed. The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions. With increasing synaptic deletion level, both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased. With gradual increasing synaptic compensation, the associative memory functions of the network were improved and the mean firing rates were increased. The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region. Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region, and can also result in memory dysfunction. To some extent, synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.
基金the Graduate School for Computing in Medicine and Life Sciences at the University of Lubeck funded by the German Research Foundation[DFG GSC 235/1]for its support.
文摘In this work, we specify potential elements of the brain to sense and regulate the energy metabolism of the organism. Our numerical investigations base on neurochemical experiments demonstrating a biphasic association between brain glucose level and neuronal activity. The dynamics of high and low affine KATP channels are most likely to play a decisive role in neuronal activity. We develop a coupled Hodgkin-Huxley model describing the interactive behavior of inhibitory GABAergic and excitatory dopaminergic neurons projecting into the caudate nucleus. The novelty in our approach is that we include the synaptic coupling of GABAergic and dopaminergic neurons as well as the interaction of high and low affine KATP channels. Both are crucial mechanisms described by kinetic models. Simulations demonstrate that our new model is coherent with neurochemical in vitro experiments. Even experimental interventions with glibenclamide and glucosamine are reproduced by our new model. Our results show that the considered dynamics of high and low affine KATP channels may be a driving force in energy sensing and global regulation of the energy metabolism, which supports central aspects of the new Selfish Brain Theory. Moreover, our simulations suggest that firing frequencies and patterns of GABAergic and dopaminergic neurons are correlated to their neurochemical outflow.
基金supported by the National Natural Science Foundation of China(Grant Nos.110020731117201711102041)
文摘This paper is concerned with bifurcations and chaos control of the Hindmarsh-Rose(HR)neuronal model with the time-delayed feedback control.By stability and bifurcation analysis,we find that the excitable neuron can emit spikes via the subcritical Hopf bifurcation,and exhibits periodic or chaotic spiking/bursting behaviors with the increase of external current.For the purpose of control of chaos,we adopt the time-delayed feedback control,and convert chaos control to the Hopf bifurcation of the delayed feedback system.Then the analytical conditions under which the Hopf bifurcation occurs are given with an explicit formula.Based on this,we show the Hopf bifurcation curves in the two-parameter plane.Finally,some numerical simulations are carried out to support the theoretical results.It is shown that by appropriate choice of feedback gain and time delay,the chaotic orbit can be controlled to be stable.The adopted method in this paper is general and can be applied to other neuronal models.It may help us better understand the bifurcation mechanisms of neural behaviors.
基金supported by the National Natural Science Foundation of China(No.11672122)the Hongliu First-Class Disciplines Development Program of Lanzhou University of Technology,China。
文摘Biological neurons can receive inputs and capture a variety of external stimuli,which can be encoded and transmitted as different electric signals.Thus,the membrane potential is adjusted to activate the appropriate firing modes.Indeed,reliable neuron models should take intrinsic biophysical effects and functional encoding into consideration.One fascinating and important question is the physical mechanism for the transcription of external signals.External signals can be transmitted as a transmembrane current or a signal voltage for generating action potentials.We present a photosensitive neuron model to estimate the nonlinear encoding and responses of neurons driven by external optical signals.In 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 source.The photocell-coupled FHN neuron can therefore capture and encode external optical signals,similar to artificial eyes.We also present detailed bifurcation analysis for estimating the mode transition and firing pattern selection of neuronal electrical activities.The 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 circuit.These results could be helpful in giving possible guidance for studying neurodynamics and applying neural circuits to detect optical signals.
