Pre-B?tzinger复合体的从簇到峰放电的同步转迁及分岔机制

Pre-Bötzinger复合体是兴奋性耦合的神经元网络,通过产生复杂的放电节律和节律模式的同步转迁参与调控呼吸节律.本文选用复杂簇和峰放电节律的单神经元数学模型构建复合体模型,仿真了与生物学实验相关的多类同步节律模式及其复杂转迁历程,并利用快慢变量分离揭示了相应的分岔机制.当初值相同时,随着兴奋性耦合强度的增加,复合体模型依次表现出完全同步的“fold/homoclinic”,“subHopf/subHopf”簇放电和周期1峰放电.当初值不同时,随耦合强度增加,表现为由“fold/homoclinic”,到“fold/fold limit cycle”、到“subHopf/subHopf”与“fold/fold limit cycle”的混合簇放电、再到“subHopf/subHopf”簇放电的相位同步转迁,最后到反相同步周期1峰放电.完全(同相)同步和反相同步的周期1节律表现出了不同分岔机制.反相峰同步行为给出了与强兴奋性耦合容易诱发同相同步这一传统观念不同的新示例.研究结果给出了preBötzinger复合体的从簇到峰放电节律的同步转迁规律及复杂分岔机制,反常同步行为丰富了非线性动力学的内涵.     

三个不同时间尺度的电耦合模型的组合簇放电

胰岛中间隙连接的胰腺β细胞的簇放电行为对胰岛素分泌起着重要的作用. 本文利用了最小的phantom 簇放电模型, 研究两个电耦合胰腺β细胞具有簇同步的组合簇放电, 其膜电位表现出一个长簇和几个短簇组成的放电簇集和振幅先减小后增大的小振幅阈下振荡的相互转迁. 在两个慢变量和快的膜电位的三维空间中, 分别考虑了中慢变量和慢慢变量作为分岔参数的多层次的快慢动力学分析, 研究这两个时间尺度不同的慢变量如何共同或单独地控制着这种组合簇放电的复杂动力学行为. 特别地, 探讨了耦合强度引起的组合簇放电的每个簇集中短簇个数变化的内在机理. 关键词： 电耦合 具有不同时间尺度的慢变量 组合簇放电 快慢动力学分析     

Hindmarsh-Rose神经元的随机自共振和噪声影响下的同步

研究了噪声对Hindmarsh-Rose(HR)神经元随机自共振和同步的影响。将高斯白噪声加入HR神经元模型的膜电位上，把外界直流电作为分岔参数，分别考虑参数处于Hopf分岔前、Hopf分岔附近和Hopf分岔后时，噪声影响下的随机自共振现象。两个未经耦合的全同HR神经元，如果接受相同的噪声激励，只要噪声强度高于某临界值，就能达到完全同步。进一步，噪声能够增强弱耦合神经元的完全同步。数值结果表明簇放电的神经元比峰放电的神经元更容易被噪声诱导而达到完全同步，耦合也增强了神经元对噪声激励的灵敏度。     

慢变控制下Chen系统的复杂行为及其机理

由于Chen系统的控制分析大都是基于同一时间尺度,而两时间尺度耦合问题的相关研究基本上局限于单维慢变量情形.本文探讨了基于慢时间尺度上的Duffing振子,即含有两维慢子系统控制下Chen系统的动力学演化过程.给出了诸如对称式fold/fold、对称式fold/Hopf、对称式homoclinic/homoclinic等不同形式的簇发振荡行为,并揭示了其相应的产生机制,指出慢子系统中两维慢变量的相互影响导致系统产生了类似于周期激励下的簇发行为.     

耦合Hindmarsh-Rose神经元的放电模式和完全同步

通过数值模拟和分岔分析的方法研究了Hindmarsh-Rose（HR）神经元的放电模式。当外加直流激励变化时，单个的神经元表现为静息态、周期性峰放电、周期性簇放电以及混沌的放电模式。利用快慢动力学分析的方法研究了HR神经元的动力学行为。当每个神经元表现为静息态、周期性放电和混沌时，两个耦合的神经元在一定的耦合强度下均会达到完全同步。神经元的耦合方式模拟神经元之间缝隙连接的电耦合。理论分析了完全同步的判断准则，并给出相应的数值模拟结果。电耦合HR神经元耦合系统的峰峰间期的分岔结构在耦合的作用下仍然能保持未耦合时的分岔结构。     

电磁辐射诱发神经元放电节律转迁的动力学行为研究

本文首先根据能量转换理论建立了电磁辐射影响下神经元电流变量模型, 然后结合Hodgkin-Huxley(HH)神经元模型研究了电磁辐射对单个神经元以及耦合神经元放电行为的影响. 结果表明, 随着电磁辐射强度的增大, 神经元放电率逐渐减小, 最后达到一个比较稳定的值. 神经元原有的周期型放电由于辐射强度的增大而逐步过渡到簇放电状态, 并借助动态分岔理论解释了这种放电模式的转换. 同时证明了磁辐射对单个神经元放电的影响可以通过神经元间的耦合传递到临近其他神经元中.     

快慢型超混沌Lorenz系统分析

讨论了快慢两时间尺度下超混沌Lorenz系统原点的稳定性问题,分析了原点的Hopf分岔,包括Hopf分岔的存在性,分岔方向以及分岔周期解的稳定性等问题,并用数值例子对所得到的结果加以验证.在一定的参数条件下,快慢系统会产生对称簇发并能达到超混沌状态.基于快慢分析法,揭示了对称簇发中沉寂态与激发态相互转迁的不同分岔模式,并进一步分析了耦合强度对慢过效应的影响. 关键词： 超混沌Lorenz系统 Hopf分岔 对称式fold/subHopf簇发 慢过效应     

双参数分岔平面内胰腺β细胞的簇放电分析

胰岛分泌胰岛素的放电活动以动作电位的簇放电为主要特点.文章考虑具有代表性且较为简单的Vries-Sherman模型,通过其快子系统的双参数分岔分析确定了双参数平面内不同簇放电类型的存在区域,并应用快慢动力学分析研究了参数v_m取不同值时所产生的簇放电模式的拓扑类型以及它们之间相互转迁的动力学机理. 关键词： 簇放电 快慢动力学 余维-2分岔     

电磁场耦合忆阻Izhikevich神经网络电活动研究

考虑到电磁场的影响,在Izhikevich神经元模型中引入电场变量和磁通变量,利用电突触耦合构建神经网络,研究电磁场耦合忆阻Izhikevich神经网络集体动力学行为。数值仿真发现：随着电突触耦合强度的增大,神经网络逐渐达到同步状态,并且神经元的放电模式也会随之改变。增大磁场耦合值可以提高神经元的放电活性,并且对网络同步也有一定的促进作用,而增大电场则会抑制神经元的放电活动。另外,当电突触与磁场耦合共同作用时,磁场耦合值越小,电突触耦合更能有效促进网络同步;在电突触耦合强度的作用下,电场抑制电活动的效果更明显。研究结果可望为理解神经系统中的信号编码和传递提供新的见解。     

基于麦克斯韦电磁场理论的神经元动力学响应与隐藏放电控制

钙、钾、钠等离子在细胞内连续泵送和传输时产生的时变电场不仅会影响神经元的放电活动,而且会诱导时变磁场去进一步调节细胞内离子的传播.根据麦克斯韦电磁场理论,时变的电场和磁场在细胞内外的电生理环境中会相互激发而产生电磁场.为了探究电磁场影响下的神经元放电节律转迁,本文在三维Hindmarsh-Rose(HR)神经元模型的基础上,引入磁通变量和电场变量,建立了一个五维HR神经元模型(简称EMFN模型).首先,结合Matcont软件分析了EMFN模型的平衡点分布与全局分岔性质,发现并分析了该模型存在的亚临界Hopf分岔、隐藏放电及其周期放电与静息态共存等现象.其次,利用双参数及单参数分岔、ISI分岔和最大Lyapunov指数等工具进行数值仿真,详细分析了EMFN模型存在的伴有混沌及无混沌的加周期分岔结构、混合模式放电和共存模式放电等现象,同时揭示了电场和磁场强度影响其放电节律的转迁规律.最后,利用Washout控制器将EMFN模型的亚临界Hopf分岔转化为超临界Hopf分岔,使其在分岔点附近的拓扑结构发生改变,由此达到消除其隐藏放电的目的.本文的研究结果证实了新建神经元模型具有丰富的放电节律,将影响神经元的信息传递和编码,为完善神经元模型,揭示电磁场对生物神经系统的影响,以及探求一些神经性疾病的致病机理提供了思路.     

Synchronization of bursting neurons: what matters in the network topology

We study the influence of coupling strength and network topology on synchronization behavior in pulse-coupled networks of bursting Hindmarsh-Rose neurons. Surprisingly, we find that the stability of the completely synchronous state in such networks only depends on the number of signals each neuron receives, independent of all other details of the network topology. This is in contrast with linearly coupled bursting neurons where complete synchrony strongly depends on the network structure and number of cells. Through analysis and numerics, we show that the onset of synchrony in a network with any coupling topology admitting complete synchronization is ensured by one single condition.     

Mesoscale and clusters of synchrony in networks of bursting neurons

We study the role of network architecture in the formation of synchronous clusters in synaptically coupled networks of bursting neurons. We give a simple combinatorial algorithm that finds the largest synchronous clusters from the network topology. We demonstrate that networks with a certain degree of internal symmetries are likely to have cluster decompositions with relatively large clusters, leading potentially to cluster synchronization at the mesoscale network level. We also address the asymptotic stability of cluster synchronization in excitatory networks of Hindmarsh-Rose bursting neurons and derive explicit thresholds for the coupling strength that guarantees stable cluster synchronization.     

Parameter-dependent synchronization transition of coupled neurons with co-existing spiking and bursting

A firing pattern transition is simulated in the Leech neuron model, firstly from bursting to co-existence of spiking and bursting and then to spiking. The attraction domain of spiking and bursting for three different parameter values are calculated. Synchronization transition processes of two coupled Leech neurons, one is bursting and the other the co-existing spiking, are simulated for the three parameters. The three synchronization processes appear similar as the coupling strength increases, beginning from non-synchronization to complete synchronization through a complex dynamical procedure, but their detailed processes are different depending on the parameter values. The transition procedure is complex and the complete synchronization is in bursting for larger parameter values, while the process is simple with complete synchronization of spiking for smaller values. The potential relationship between complete synchronization and the attraction domain is also discussed. The results are instructive to understanding the synchronization behaviors of the coupled neuronal system with co-existing attractors.     

Transitions to synchrony in coupled bursting neurons

Certain cells in the brain, for example, thalamic neurons during sleep, show spike-burst activity. We study such spike-burst neural activity and the transitions to a synchronized state using a model of coupled bursting neurons. In an electrically coupled network, we show that the increase of coupling strength increases incoherence first and then induces two different transitions to synchronized states, one associated with bursts and the other with spikes. These sequential transitions to synchronized states are determined by the zero crossings of the maximum transverse Lyapunov exponents. These results suggest that synchronization of spike-burst activity is a multi-time-scale phenomenon and burst synchrony is a precursor to spike synchrony.     

Coherence resonance and synchronization of Hindmarsh-Rose neurons with noise

Noise effects on coherence resonance and synchronization of Hindmarsh-Rose (HR) neuron model are studied. The coherence resonance of a single HR neuron with Gaussian white noise added to the membrane potential is investigated in situations before, near and after the Hopf bifurcation, separately, with the external direct current as a bifurcation parameter. It is shown that even though there is no coupling between neurons, uncoupled identical HR neurons driven by a common noise can achieve complete synchronization when the noise intensity is higher than a critical value. Furthermore, noise also enhances complete synchronization of weakly coupled neurons. It is concluded that synchronization in bursting neurons is easier to be induced than in spiking ones, and coupling enhances the sensitivity of synchronization of neurons to noise stimulus.     

Origin of bursting through homoclinic spike adding in a neuron model

The origin of spike adding in bursting activity is studied in a reduced model of the leech heart interneuron. We show that, as the activation kinetics of the slow potassium current are shifted towards depolarized membrane potential values, the bursting phase accommodates incrementally more spikes into the train. This phenomenon is attested to be caused by the homoclinic bifurcations of a saddle periodic orbit setting the threshold between the tonic spiking and quiescent phases of the bursting. The fundamentals of the mechanism are revealed through the analysis of a family of the onto Poincaré return mappings.     

Intermittent synchronization in a network of bursting neurons

Synchronized oscillations in networks of inhibitory and excitatory coupled bursting neurons are common in a variety of neural systems from central pattern generators to human brain circuits. One example of the latter is the subcortical network of the basal ganglia, formed by excitatory and inhibitory bursters of the subthalamic nucleus and globus pallidus, involved in motor control and affected in Parkinson's disease. Recent experiments have demonstrated the intermittent nature of the phase-locking of neural activity in this network. Here, we explore one potential mechanism to explain the intermittent phase-locking in a network. We simplify the network to obtain a model of two inhibitory coupled elements and explore its dynamics. We used geometric analysis and singular perturbation methods for dynamical systems to reduce the full model to a simpler set of equations. Mathematical analysis was completed using three slow variables with two different time scales. Intermittently, synchronous oscillations are generated by overlapped spiking which crucially depends on the geometry of the slow phase plane and the interplay between slow variables as well as the strength of synapses. Two slow variables are responsible for the generation of activity patterns with overlapped spiking, and the other slower variable enhances the robustness of an irregular and intermittent activity pattern. While the analyzed network and the explored mechanism of intermittent synchrony appear to be quite generic, the results of this analysis can be used to trace particular values of biophysical parameters (synaptic strength and parameters of calcium dynamics), which are known to be impacted in Parkinson's disease.