Parameter space of the Rulkov chaotic neuron model

The parameter space of the two dimensional Rulkov chaotic neuron model is taken into account by using the qualitative analysis, the co-dimension 2 bifurcation, the center manifold theorem, and the normal form. The goal is intended to clarify analytically different dynamics and firing regimes of a single neuron in a two dimensional parameter space. Our research demonstrates the origin that there exist very rich nonlinear dynamics and complex biological firing regimes lies in different domains and their boundary curves in the two dimensional parameter plane. We present the parameter domains of fixed points, the saddle-node bifurcation, the supercritical/subcritical Neimark–Sacker bifurcation, stability conditions of non hyperbolic fixed points and quasiperiodic solutions. Based on these parameter domains, it is easy to know that the Rulkov chaotic neuron model can produce what kinds of firing regimes as well as their transition mechanisms. These results are very useful for building-up a large-scale neuron network with different biological functional roles and cognitive activities, especially in establishing some specific neuron network models of neurological diseases.     

Dynamic transition on the seizure-like neuronal activity by astrocytic calcium channel block

The involvement of astrocytes in neuronal firing dynamics is becoming increasingly evident. In this study, we used a classical hippocampal tripartite synapse model consisting of soma-dendrite coupled neuron models and a Hodgkin–Huxley-like astrocyte model, to investigate the seizure-like firing in the somatic neuron induced by the over-expressed neuronal N-methyl-d-aspartate (NMDA) receptors. Based on this model, we further investigated the effect of the astrocytic channel block on the neuronal firing through a bifurcation analysis. Results show that blocking inositol-1,4,5-triphosphate(IP3)-dependent calcium channel in astrocytes efficiently suppresses the astrocytic calcium oscillation, which in turn suppresses the seizure-like firing in the neuron.     

A neural network approach to solve the stable matching problem

In this paper two types of neurons, the maximum selection neuron and the maximum cut-off neuron are introduced. They are used to construct a neural network to represent and solve the stable matching problem. The neural network approach allows the matching to be processed dynamically in a distributed parallel processing environment.     

FPGA Realization of Fractional Order Neuron

In this paper fractional Hindmarsh Rose (HR) neuron, which mimics several behaviors of a real biological neuron is implemented on field programmable gate array (FPGA). The results show several differences in the dynamic characteristics of integer and fractional order Hindmarsh Rose neuron models. The integer order model shows only one type of firing characteristics when the parameters of model remains same. The fractional order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied. The firing frequency increases when the order of the fractional operator decreases. The fractional order is therefore key in determining the firing characteristics of biological neurons. To implement this neuron model first the digital realization of different fractional operator approximations are obtained, then the fractional integrator is used to obtain the low power and low cost hardware realization of fractional HR neuron. The fractional neuron model has been implemented on a low voltage and low power circuit and then compared with its integer counter part. The hardware is used to demonstrate the different dynamical behaviors of fractional HR neuron for different type of approximations obtained for fractional operator in this paper. A coupled network of fractional order HR neurons is also implemented. The results also show that synchronization between neurons increases as long as coupling factor keeps on increasing.     

“神经元的形态分类和识别”综述

人类脑计划得到神经元的形态数据,网站NeuroMorpho.Org提供了世界各地56个实验室,11类动物,45个神经区域,47类功能类别的6214个神经元的三维空间形态数据.题目提出的问题是利用这些数据,如何给出一个神经元形态分类方法,并利用你给出的神经元几何形态分类方法,具体识别待定神经元的类型,提出神经元模型命名方法.     

On the complexity of recognizing a set of vectors by a neuron

We consider the problems connected with the computational abilities of a neuron. The orderings of finite subsets of real vectors associated with neural computing are studied. We construct a lattice of such orderings and study some its properties. The interrelation between the orders on the sets and the neuron implementation of functions defined on these sets is derived. We prove the NP-hardness of “The Shortest Vector” problem and represent the relationship of the problem with neural computing.     

Modeling the bursting effect in neuron systems

We propose a new method for modeling the well-known phenomenon of “bursting behavior” in neuron systems by invoking delay equations. Namely, we consider a singularly perturbed nonlinear difference-differential equation with two delays describing the functioning of an isolated neuron. Under a suitable choice of parameters, we establish the existence of a stable periodic motion with any prescribed number of spikes on a closed time interval equal to the period length.     

Dynamical behavior of delayed Hopfield neural networks with discontinuous activations

In this paper, a general class of neural networks with arbitrary constant delays is studied, whose neuron activations are discontinuous and may be unbounded or nonmonotonic. Based on the Leray–Schauder alternative principle and generalized Lyapunov approach, conditions are given under which there is a unique equilibrium of the neural network, which is globally asymptotically stable. Moreover, the existence and global asymptotic stability of periodic solutions are derived, where the neuron inputs are periodic. The obtained results extend previous works not only on delayed neural networks with Lipschitz continuous neuron activations, but also on delayed neural networks with discontinuous neuron activations.     

Traveling wave solutions of diffusive Hindmarsh–Rose-type equations with recurrent neural feedback

From the perspective of bifurcation theory, this study investigates the existence of traveling wave solutions for diffusive Hindmarsh–Rose-type (dHR-type) equations with recurrent neural feedback (RNF). The applied model comprises two additional terms: 1) a diffusion term for the conduction process of action potentials and 2) a delay term. The delay term is introduced because if a neuron excites a second neuron, the second neuron, in turn, excites or inhibits the first neuron. To probe the existence of traveling wave solutions, this study applies center manifold reduction and a normal form method, and the results demonstrate the existence of a heteroclinic orbit of a three-dimensional vector for dHR-type equations with RNF near a fold–Hopf bifurcation. Finally, numerical simulations are presented.     

Non-parametric estimation of the spiking rate in systems of interacting neurons

We consider a model of interacting neurons where the membrane potentials of the neurons are described by a multidimensional piecewise deterministic Markov process with values in \({\mathbb {R}}^N, \) where N is the number of neurons in the network. A deterministic drift attracts each neuron’s membrane potential to an equilibrium potential m. When a neuron jumps, its membrane potential is reset to a resting potential, here 0,  while the other neurons receive an additional amount of potential \(\frac{1}{N}.\) We are interested in the estimation of the jump (or spiking) rate of a single neuron based on an observation of the membrane potentials of the N neurons up to time t. We study a Nadaraya–Watson type kernel estimator for the jump rate and establish its rate of convergence in \(L^2 .\) This rate of convergence is shown to be optimal for a given Hölder class of jump rate functions. We also obtain a central limit theorem for the error of estimation. The main probabilistic tools are the uniform ergodicity of the process and a fine study of the invariant measure of a single neuron.     

Vector Wirtinger-type inequality and the stability analysis of delayed neural network

This paper proposes some new stability criteria for a class of delayed neural networks with sector and slope restricted nonlinear neuron activation function. By using the convex express of the nonlinear neuron activation function, the original delayed neural network is transformed into a linear uncertain system. The proposed method employs an improved vector Wirtinger-type inequality for constructing a novel Lyapunov functional. Based on the Lyapunov stable theory, new delay-dependent and delay-independent stability criteria for the researched system are established in terms of linear matrix inequality technique, delay partitioning approach and characteristic root method. Three illustrative examples are presented to verify the effectiveness of the main results.     

因素空间理论与知识表示的数学框架（X）——基于因素空间的神经元模型

本文是文［１—９］的继续，该文及后续论文仍将系统地研究因素空间理论及其在知识表示中的应用。该文研究基于因素空间的神经元模型：首先讨论了因素空间的神经元机理，然后讨论了几种典型的神经元模型；特别提出了基于Ｗｅｂｅｒ—Ｆｅｃｈｎｅｒ法则的神经元模型以及基于变权的神经元模型。     

Controlling chaos in space-clamped FitzHugh–Nagumo neuron by adaptive passive method

The space-clamped FitzHugh–Nagumo (SCFHN) neuron exhibits complex chaotic firing when the amplitude of the external current falls into a certain area. To control the undesirable chaos in SCFHN neuron, a passive control law is presented in this paper, which transforms the chaotic SCFHN neuron into an equivalent passive system. It is proved that the equivalent system can be asymptotically stabilized at any desired fixed state, namely, chaos in SCFHN neuron can be controlled. Moreover, to eliminate the influence of undeterministic parameters, an adaptive law is introduced into the designed controller. Computer simulation results show that the proposed controller is very effective and robust against the uncertainty in systemic parameters.     

Control of a dendritic neuron driven by a phase-independent stimulation

A dendritic neuron model exhibits bistability under continuous weak stimulation – the oscillatory synchronized regime and the quiet regime coexist. Complex nonlinear dynamics is observed when the neuron undergoes not only phase-dependent continuous weak stimulation, but also when it is driven by an external phase-independent stimulation. In the latter case basin boundaries between the synchronized and the quiet regime become complex and fractal. Simple strategies based on control pulses are not sufficient in these circumstances, because it becomes difficult to predict the dynamics of the neuron after the application of the control pulse. Therefore, a new neural control method is proposed. Initially, a weak phase control strategy is applied until fractal basin boundaries evolve into a deterministic manifold. Consequently, a single control pulse is immediately applied and the neuron evolves into the calm state.     

Mathematical Modeling and Analysis of Spatial Neuron Dynamics: Dendritic Integration and Beyond

Neurons compute by integrating spatiotemporal excitatory (E) and inhibitory (I) synaptic inputs received from the dendrites. The investigation of dendritic integration is crucial for understanding neuronal information processing. Yet quantitative rules of dendritic integration and their mathematical modeling remain to be fully elucidated. Here neuronal dendritic integration is investigated by using theoretical and computational approaches. Based on the passive cable theory, a PDE-based cable neuron model with spatially branched dendritic structure is introduced to describe the neuronal subthreshold membrane potential dynamics, and the analytical solutions in response to conductance-based synaptic inputs are derived. Using the analytical solutions, a bilinear dendritic integration rule is identified, and it characterizes the change of somatic membrane potential when receiving multiple spatiotemporal synaptic inputs from the dendrites. In addition, the PDE-based cable neuron model is reduced to an ODE-based point-neuron model with the feature of bilinear dendritic integration inherited, thus providing an efficient computational framework of neuronal simulation incorporating certain important dendritic functions. The above results are further extended to active dendrites by numerical verification in realistic neuron simulations. Our work provides a comprehensive and systematic theoretical and computational framework for the study of spatial neuron dynamics. © 2021 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.     

LMI conditions for global robust stability of delayed neural networks with discontinuous neuron activations

Without assuming that the neuron activations are bounded, some delay-independent criteria for interval delayed neural networks with discontinuous neuron activations are derived to guarantee global robust stability by using the generalized Lyapunov method and linear matrix inequality (LMI) technique. The obtained results improve and extend those given in earlier literature, and two numerical examples are also given to show the effectiveness of our results.     

Functional motifs: a novel perspective on burst synchronization and regularization of neurons coupled via delayed inhibitory synapses

For one of the most common network motifs, an inhibitory neuron pair, we perform an extensive study of burst synchronization and the related phenomena applying the model of Rulkov maps coupled via delayed synapses. Instigated by the phase-plane analysis, that has the neuron switching between the noninteracting and the interacting map, it is demonstrated how the system evolution may be interpreted by means of the dynamical configurations of the motif, each represented by an extracted subgraph. Under the variation of the synaptic parameters, the probability of finding synchronized neurons in a given configuration is seen to reflect the way in which the anti-phase synchronization is eventually superseded by the synchronization in phase. Such an approach also provides a novel insight into regularization, characterizing the neuron bursting in either of these regimes. Looking into correlation of the two neurons’ bursting cycles we acquire a deeper understanding of the more sophisticated mechanisms by which the regularity in the time series is maintained. Further, it is examined whether introducing heterogeneity in the neuron or the synaptic parameters may prove advantageous over the homogeneous case with respect to burst synchronization.     

时滞对磁通耦合及化学耦合神经元分岔及同步的影响

以化学突触耦合神经元模型为基础,讨论了抑制性及兴奋性条件下达到同步的区别及同步的类型。并根据磁通耦合对神经元放电的影响,讨论了具有时滞、磁通耦合和化学耦合Morris-Lecar (ML)神经元模型的放电状态、分岔类型及其同步情况。发现具有磁通耦合和化学耦合ML神经元系统在不同参数下会产生丰富的逆倍周期分岔或加周期分岔行为。而时滞的引入,虽然可以增加系统的周期性,但同时也会破环系统同步。相反,适当的耦合强度能够增加同步。     

The effects of time delay on the stochastic resonance in feed-forward-loop neuronal network motifs

The dependence of stochastic resonance in the feed-forward-loop neuronal network motifs on the noise and time delay are studied in this paper. By computational modeling, Izhikevich neuron model with the chemical coupling is used to build the triple-neuron feed-forward-loop motifs with all possible motif types. Numerical results show that the correlation between the periodic subthreshold signal’s frequency and the dynamical response of the network motifs is resonantly dependent on the intensity of additive spatiotemporal noise. Interestingly, the excitatory intermediate neuron could induce intermittent stochastic resonance, whereas the inhibitory one weakens its influence on the intermittent mode. More importantly, it is found that the increasing delays can induce the intermittent appearance of regions of stochastic resonance. Based on the effects of the time delay on the stochastic resonance, the reasons and conditions of such intermittent resonance phenomenon are analyzed.     

Phase-locking and chaos in a silent Hodgkin–Huxley neuron exposed to sinusoidal electric field

Neuronal firing patterns are related to the information processing in neural system. This paper investigates the response characteristics of a silent Hodgkin–Huxley neuron to the stimulation of externally-applied sinusoidal electric field. The neuron exhibits both p:q phase-locked (i.e. a periodic oscillation defined as p action potentials generated by q cycle stimulations) and chaotic behaviors, depending on the values of stimulus frequencies and amplitudes. In one-parameter space, a rich bifurcation structure including period-adding without chaos and phase-locking alternated with chaos suggests frequency discrimination of the neuronal firing patterns. Furthermore, by mapping out Arnold tongues, we partition the amplitude–frequency parameter space in terms of the qualitative behaviors of the neuron. Thus the neuron’s information (firing patterns) encodes the stimulus information (amplitude and frequency), and vice versa.