Accounting for the energies and entropies of kinesin’s catalytic cycle

When the processive motor protein kinesin walks along the biopolymer microtubule it can occasionally make a backward step. Recent single molecule experiments on moving kinesin have revealed that the forward-to-backward step ratio decreases exponentially with the load force. Carter and Cross (Nature 435, 308-312, 2005) found that this ratio tightly followed 802 × exp[−0.95F], where F is the load force in piconewtons. A straightforward analysis of a Brownian step leads to L/(2k _B T) as the factor in front of the load force, where L is the 8 nm stepsize, k _B is the Boltzmann constant, and T is the temperature. The factor L/(2k _B T) does indeed equal 0.95 pN⁻¹. The same analysis shows how the 802 prefactor derives from the power stroke energy G as exp[G/(2k _B T)]. There are indications that the power stroke derives from the entropically driven coiling of the 30 amino acid neck linker that connects the two kinesin heads. This idea is examined and consequences are deduced.     

Synchronization transition in gap-junction-coupled leech neurons

Real neurons can exhibit various types of firings including tonic spiking, bursting as well as silent state, which are frequently observed in neuronal electrophysiological experiments. More interestingly, it is found that neurons can demonstrate the co-existing mode of stable tonic spiking and bursting, which depends on initial conditions. In this paper, synchronization in gap-junction-coupled neurons with co-existing attractors of spiking and bursting firings is investigated as the coupling strength gets increased. Synchronization transitions can be identified by means of the bifurcation diagram and the correlation coefficient. It is illustrated that the coupled neurons can exhibit different types of synchronization transitions between spiking and bursting when the coupling strength increases. In the course of synchronization transitions, an intermittent synchronization can be observed. These results may be instructive to understand synchronization transitions in neuronal systems.     

Effect of astrocyte on synchronization of thermosensitive neuron-astrocyte minimum system

Astrocytes have a regulatory function on the central nervous system (CNS), especially in the temperature-sensitive hippocampal region. In order to explore the thermosensitive dynamic mechanism of astrocytes in the CNS, we establish a neuron-astrocyte minimum system to analyze the synchronization change characteristics based on the Hodgkin-Huxley model, in which a pyramidal cell and an interneuron are connected by an astrocyte. The temperature range is set as 0 ℃-40 ℃ to juggle between theoretical calculation and the reality of a brain environment. It is shown that the synchronization of thermosensitive neurons exhibits nonlinear behavior with changes in astrocyte parameters. At a temperature range of 0 ℃-18 ℃, the effects of the astrocyte can provide a tremendous influence on neurons in synchronization. We find the existence of a value for inositol triphosphate (IP₃) production rate and feedback intensities of astrocytes to neurons, which can ensure the weak synchronization of two neurons. In addition, it is revealed that the regulation of astrocytes to pyramidal cells is more sensitive than that to interneurons. Finally, it is shown that the synchronization and phase transition of neurons depend on the change in Ca²⁺ concentration at the temperature of weak synchronization. The results in this paper provide some enlightenment on the mechanism of cognitive dysfunction and neurological disorders with astrocytes.     

周期激励下的前包钦格呼吸神经元的簇振荡现象研究

研究周期激励作用下的非自治前包钦格呼吸神经元模型,结果表明当外界激励频率与系统固有频率存在着量级差距时,系统可以产生典型的簇放电模式.由于激励频率远小于系统的固有频率,因此将整个周期激励项视为慢变参数,从而可以利用稳定性分析理论研究慢变参数变化下的平衡点的分岔类型,进一步应用快慢动力学分析方法给出簇模式产生的动力学机理.本文的结果说明外界激励对神经元的动力学行为有着重要影响,为进一步揭示呼吸节律的产生机制提供了重要帮助.     

基于单神经元控制器交流 PWM 调速系统

针对常用的单神经元控制方法在交流ＰＷＭ调速系统中应用存在的问题,提出一种基于增量式单神经元控制方法,仿真结果表明该方法有效提高了控制效果和系统性能,且控制器结构简单,适于工程应用．     

输入—状态线性化控制永磁同步电动机混沌系统

采用输入-状态线性化方法控制永磁同步电动机系统中出现的混沌现象,利用输入-状态可线性化的能控条件和对合条件对施加控制的混沌系统进行判断,当满足线性化条件时,运用微分几何中的Lie导数和Lie括号运算将非线性系统模型转化为线性模型,然后对线性化后的系统设计控制器,使系统的三个状态变量均稳定的收敛于零,从而消除了混沌,仿真结果证明了该方法的有效性。     

一类两神经元时滞神经网络稳定性和Hopf分支

研究了一类具有两个神经元两个时滞的时滞神经网络模型.以两个时滞的各种组合为参数,研究了模型的局部稳定性和Hopf分支,得到模型的局部稳定性和产生局部Hopf分支的充分条件.最后,给出仿真实例,验证了理论分析结果的正确性.     

基于神经网络模糊控制理论的转台伺服系统控制设计

针对三轴飞行仿真转台伺服系统非线性、模型不精确等特点,在分析转台系统结构的基础上,采用模糊PID控制的方法对转台伺服系统进行仿真控制,得到较好的控制效果。模糊控制控制规则的获得带有很大的人为因素,并且在控制过程中对规则采用查表法占用大量的内存。基于以上原因,设计了神经网络模糊控制器(NNSOC),利用神经网络控制自学习、自调整的能力,为模糊控制器提供自动生成控制规则的能力;同时由于神经网络具有联系记忆能力,可对未训练的样本做出决策。对NNSOC的控制效果进行了仿真。结果表明：其具有很好的动态性能和鲁棒性,对转台的控制效果良好。     

FitzHugh-Nagumo神经元网络的联想记忆与分割

以广泛讨论的Fitz Hugh-Nagumo神经元节点组成脉动神经元网络,从神经系统空时模式编码理论研究网络的记忆(或模式)存储与时间分割问题.给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地分割出每一种模式.如果输入的模式有缺损,系统能够把它们恢复成原型,即神经网络的联想记忆功能.模拟需要调节耦合强度和噪声强度等参数使得网络在特定的参数值和中等强度噪声达到最优的时间分割,与广泛讨论的随机共振现象一致.     

脑认知功能的理论框架与方法学基础

在回顾80年代以前的一些重大理论并将之归纳为５个理论要点的基础上,综述了近年出现的新理论、新技术和新事实,说明脑认知功能研究正在发生重大历史转折。分析了各研究层次上的新进展和重大难题,包括分子神经生物学、细胞生理学、脑功能系统和网络研究、脑成像技术、心理学研究、认知理论和计算研究。提出以脑能量效率为量纲,沟通不同层次、不同学科的研究工作,并设想了这类跨学科研究的途径和方法,希望通过跨学科研究能形成脑认知功能的新理论。