Discrete-time movement model of a group of trains on a rail line with stochastic disturbance

This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway.

Robustness Analysis of Discrete-time Indirect Model Reference Adaptive Control with Normalized Adaptive Laws

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.

Anticontrol of chaos for discrete-time fuzzy hyperbolic model with uncertain parameters

This paper proposes a new method to chaotify the discrete-time fuzzy hyperbolic model （DFHM） with uncertain parameters. A simple nonlinear state feedback controller is designed for this purpose. By revised Marotto theorem, it is proven that the chaos generated by this controller satisfies the Li-Yorke definition. An example is presented to demonstrate the effectiveness of the approach.

Scheduling of high-speed rail traffic based on discrete-time movement model

In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.

Dynamics and synchronization of neural models with memristive membranes under energy coupling

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.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

Community detection in signed networks based on discrete-time model

Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.

Direct model reference adaptive control using K_p = LDU factorization for multivariable discrete-time plants

For a large class of discrete-time multivariable plants with arbitrary relative degrees, the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions. The algorithm is based on a new parametrization derived from the high frequency gain matrix factorization Kp=LDU under the condition that the signs of the leading principal minors of/fp are known. By reproving the discrete-time Lp and L2σ norm relationship between inputs and outputs, establishing the properties of discrete-time adaptive law, defining the normalizing signal, and relating the signal with all signals in the closed-loop system, the stability and convergence of the discrete-time multivariable model reference adaptive control scheme are analyzed rigorously in a systematic fashion as in the continuous-time case.

NeuronSup:基于偏见神经元抑制的深度模型去偏方法

随着深度学习的广泛应用,研究者在关注模型分类性能的同时,还需要关注模型的决策是否公平可信。存在决策偏见的深度模型会造成极大的负面影响,因此如何维持深度模型的分类正确率,同时提高模型的决策公平至关重要。目前已有工作提出了较多方法,用于改善模型的个体公平,但是这些方法仍然在去偏效果、去偏后模型可用性、去偏效率等方面存在缺陷。为此,文中分析了深度模型存在个体偏见时神经元异常激活现象,提出了一种基于偏见神经元抑制的模型去偏方法NeuronSup,具有显著降低个体偏见、对主任务性能影响小、时间复杂度低等优势。具体而言,首先根据深度模型部分神经元由于个体偏见而产生异常激活的现象提出了偏见神经元的概念。然后,利用歧视样本对查找深度模型中的偏见神经元,通过抑制偏见神经元的异常激活大幅降低深度模型的个体偏见,并且根据每个神经元的最大权重边确定主任务性能神经元,通过保持深度模型的主任务性能神经元参数不变,来减小去偏操作对深度模型分类性能造成的影响。因为NeuronSup只对深度模型中的特定神经元进行去偏操作,所以时间复杂度更低,效率更高。最后,在3个真实数据集的6种敏感属性上开展去偏实验,与5种对比算法相比,NeuronSup将个体公平指标THEMIS降低了50%以上,同时使去偏操作对深度模型分类准确率的影响降低到3%以内,验证了NeuronSup在保证深度模型分类能力的情况下降低个体偏见的有效性。

Synchronization of the minimal models of bursting neurons coupled by delayed chemical or electrical synapses

The minimal two-dimensional model of bursting neuronal dynamics is used to study the influence of time-delay on the properties of synchronization of bursting neurons. Generic properties of bursting and dependence of the stability of synchronization on the time-lag and the strength of coupling are described, and compared with the two common types of synaptical coupling, i.e., time-delayed chemical and electrical synapses.

Distinct neuronal excitability alterations of medial prefrontal cortex in early-life neglect model of rats

Object:Early-life neglect has irreversible emotional effects on the central nervous system.In this work,we aimed to elucidate distinct functional neural changes in me-dial prefrontal cortex(mPFC)of model rats.Methods:Maternal separation with early weaning was used as a rat model of early-life neglect.The excitation of glutamatergic and GABAergic neurons in rat mPFC was recorded and analyzed by whole-cell patch clamp.Results:Glutamatergic and GABAergic neurons of mPFC were distinguished by typi-cal electrophysiological properties.The excitation of mPFC glutamatergic neurons was significantly increased in male groups,while the excitation of mPFC GABAergic neurons was significant in both female and male groups,but mainly in terms of rest membrane potential and amplitude,respectively.Conclusions:Glutamatergic and GABAergic neurons in medial prefrontal cortex showed different excitability changes in a rat model of early-life neglect,which can contribute to distinct mechanisms for emotional and cognitive manifestations.

Serotonin controls axon and neuronal regeneration in the nervous system:lessons from regenerating animal models

Traumatic brain injury （TBI） is a mechanical injury to brain tissue that leads to an impairment of function and a broad spectrum of symptoms and disabilities; often, it is followed by diffuse axonal injury, which causes denaturation of the white matter and axon retraction, leaving patients with severe brain damage or even in a persistent vegetative state.

Stimulatory Effects of NMDA on Intracellular Ca^(2+) Nonlinear Kinetic Model in Rat Dorsal Root Ganglion Neurons

The present study revealed the stimulatory effects of NMDA on intracellular ca 2+ concentration in rat dorsal root ganglion (DRG) neurons. Fura 3/AM, an intracellular calcium fluorescent indicator was used to monitor the fluctuation of 〔ca 2+ 〕 i. Here we present the evidence that (1) Confocal microscopy resolved the cells of three different sizes, based on which a cell diameter distribution histogram was drawn. The fluorescence signals originated from the cells of different sizes, small size (diameter<30μm), medium size (diameter between 30 to 50μm),and large size (diameter>50μm); presumably intracellular Ca 2+ concentration was different in the cells of different sizes. (2) The time related variation of fluorescence intensity was observed. In particular, the fluorescence intensity in 0 Ca 2+ bath solution was affected by the application of high ca 2+ solution. (3) In 0 ca 2+ bath solution the intracellular Ca 2+ concentration nonlinear properties of distinct diameter cells was described. (4) A kind of SETAR model was fitted with a medium sized cell.(5)The residuals from the fitted model were tested to see whether they were plausibly Gaussian. These findings indicated that in distinct types of cells intracellular Ca 2+ concentration had different characteristics in different DRG neurons, and contributed to different functions of these neurons. Besides, the established threshold autoregressive model can share intracellular ca 2+ with the main nonlinear kinetic

A Mechanoelectrical Coupling Model of Neurons under Stretching

Introduction Neurons are situated in a microenvironment composed of various biochemical and biophysical cues,where stretching is thought to have a major impact on neurons.For instance,during a moderate traumatic brain impact,the injury region in axons exhibits significant longitudinal strain;and in a rat model of spinal cord injury,the most severe axonal injury is located in the largest strain region.Stretching may result in microstructural changes in neural tissue and further leading to abnormal electrophysiological function.Hence,it is of great importance to understand the coupled mechanoelectricalbehaviors of neurons under stretching.In spite of significant experimental efforts,the underlying mechanism remains elusive,more works are needed to provide a detailed description of the process that leads to the observed phenomena.Mathematical modeling is a powerful tool that offers a quantitative description of the underlying mechanism of an observed biological phenomenon,including mechanical and electrophysiological behaviors of neurons.Thus,we developed a mechanoelectrical coupling model of neurons under stretching in this study.Mathematical model The mathematical model consists of three submodels,i.e.,the mechanical submodel,the mechanoelectrical coupling submodel and the electrophysiological submodel.The mechanical submodel deals with the relationship between stretching and the deformation of axons,which has specially considered the plastic deformation of axons.The electrophysiological submodel characterizes the feature of neuronal action potential(AP),which is based on the classical H-H model and the cable theory.The mechanoelectrical coupling submodel links the mechanical and electrophysiological submodels through strain-induced equivalent circuit parameter alteration and ion channel injury.Besides,we have discussed a more general deformation condition,where an expanded model coupling the axonal deformation and electrophysiology alteration was explored.As the most essential parameters in an electrophysiological assessment,the amplitude of the AP,the neuronal firing frequency and the electrophysiological signal conduction velocity,which could be affected by stretching,were used as outputs of the model.Results&discussion To understand the mechanoelectrical coupling of neurons under stretching,we developed a mechanoelectrical coupling model.To verify the model,we simulated a slow stretching on an axon following the experimental study in the literature,we observed that as the strain increases,the peak AP declines faster,which is consistent with the experimental data.Moreover,the reduced AP cannot be restored to the original peak,implying that the damage is irreversible.The simulation results also predict that strain induces a more frequent neuronal firing and a faster conduction.In a realistic situation,in addition to stretching,the loading condition is very complicated,which may induce complex axonal deformation(e.g., necking and swelling along the axons).We also simulated such necking deformation impairment and observed that the AP amplitude decreases at the necking region and recovers after that,indicating a blockage of the AP;and the conduction velocity decreases with the increase in deformation degree.Conclusions In this study,we developed a mechanoelectrical coupling model of neurons under stretching with consideration of axonal plastic deformation.With the model,we found that the effect of mechanical loading on electrophysiology mainly manifests as decreased membrane AP amplitude,a more frequent neuronal firing and a faster electrophysiological signal conduction.The model predicts not only stretch-induced injury but also a more gene ral necking deformation case,which may someday be revealed in future by experiments,providing a reference for the prediction and regulation of neuronal function under mechanical loadings.

Determinants of Response Pattern of Cochlear Nucleus Neuron:a Study Based on the Model

Objective: Neurons in the cochlear nucleus show different response patterns to the short tone bursts. Because of the limitations of animal experiments, it is hard to explore the principle. Therefore, using a model to simulate CN neurons will be a feasible way. Methods: Based on the initial model mentioned in the previous study, we proposed an improved CN model in MATLAB R2012b. Results: By modifying the parameters of the model we found the interchanges among "primary-like", "chopper",and "onset" response patterns. Furthermore, we simulated the "pauser" response pattern by adding an extra input in our model. Conclusion: The results indicate that the synaptic integrations and the input modes can give rise to different characteristics of CN neurons, which eventually determine the response patterns of CN neurons.

Alleviated mucosal and neuronal damage in a rat model of Crohn's disease

AIM:To establish a rat model suitable to investigate the repetitive relapsing inflammations(RRI)characteristic to Crohn’s disease.METHODS:Colitis was induced by 2,4,6-trinitrobenzenesulfonic acid(TNBS).RRI were mimicked by repeating administrations of TNBS.Tissue samples were taken from control,once,twice and three times treated rats from the inflamed and adjacent non-inflamed colonic segments at different timepoints during the acute intestinal inflammation.The means of the ulcerated area were measured to evaluate the macroscopic mu-cosal damage.The density of myenteric neurons was determined on whole mounts by Hu C/Hu D immunohistochemistry.Heme oxygenase-1(HO-1)expression was evaluated by molecular biological techniques.RESULTS:TNBS-treated rats displayed severe colitis,but the mortality was negligible,and an increase of body weight was characteristic throughout the experimental period.The widespread loss of myenteric neurons,and marked but transient HO-1 up-regulation were demonstrated after the first TNBS administration.After repeated doses the length of the recovery time and extent of the ulcerous colonic segments were markedly decreased,and the neuronal loss was on a smaller scale and was limited to the inflamed area.HO-1 m RNA level was notably greater than after a single dose and overexpression was sustained throughout the timepoints examined.Nevertheless,the HO-1protein up-regulation after the second TNBS treatment proved to be transient.Following the third treatment HO-1 protein expression could not be detected.CONCLUSION:Experimentally provoked RRI may exert a protective preconditioning effect against the mucosal and neuronal damage.The persistent up-regulation of HO-1 m RNA expression may correlate with this.

A New Kind of Artificial Neuron Models

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Using induced pluripotent stem cells derived neurons to model brain diseases

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.

Numerical Simulation Analysis of a Mathematical Model of Circadian Pacemaker Neurons

Sim and Forger have proposed a mathematical model of circadian pacemaker neurons in the suprachiasmatic nucleus (SCN). This model, which has been formulated on the Hodgkin-Huxley mo-del, is described by a system of nonlinear ordinary differential equations. An important feature of the SCN neurons observed in electrophysiological recording is spontaneous repetitive spiking, which is reproduced using this model. In the present study, numerical simulation analysis of this model was performed to evaluate variations in two system parameters of this model: the maximal conductance of calcium current (gCa) and the maximal conductance of sodium current (gNa). Simulation results revealed the spontaneous repetitive spiking states of the model in the (gCa, gNa)-pa-rameter space.

Modeling the Spike Response for Adaptive Fuzzy Spiking Neurons with Application to a Fuzzy XOR

A spike response model(SRM)based on the spikes generator circuit(SGC)of adaptive fuzzy spiking neurons(AFSNs)is developed.The SRM is simulated in MatlabTM environment.The proposed model is applied to a configuration of a fuzzy exclusive or(fuzzy XOR)operator,as an illustrative example.A description of the comparison of AFSNs with other similar methods is given.The novel method of the AFSNs is used to determine the value of the weights or parameters of the fuzzy XOR,first with dynamic weights or self-tuning parameters that adapt continuously,then with fixed weights obtained after training,finally with fixed weights and a dynamic gain or self-tuning gain for a fine adjustment of amplitude.