双层Hodgkin-Huxley神经元网络中的随机共振

随机共振是一种非零噪声优化系统响应的现象。运用信噪比的评价方式,研究单个Hodgkin-Huxley神经元及其所构建的双层神经元网络中的随机共振,来模拟生物感觉系统中检测微弱信号的随机共振现象。结果表明,单个神经元在阈值下存在噪声优化系统检测性能的随机共振现象,但是最优的噪声强度却随外部信号性质的改变而变化;双层神经元网络不但可以在固定的噪声强度上对一定幅度范围内的阈下信号进行优化检测,而且噪声的存在并没有降低网络对阈上信号的检测能力。     

电突触耦合神经元的相位同步及放电节律的转化

研究了两个参数失配较大情况下,处于不同放电模式的两个电突触耦合Hindmarsh-rose(HR)神经元的相位同步问题,发现在适当耦合强度下可以实现相同步并呈现出复杂的放电节律.利用峰峰间期(Interspikeinterval,ISI)和平均放电频率证实了相同步的发生,给出并分析了不同放电状态的神经元在电突触耦合下实现相同步后的神经放电节律.从相同步的角度显示,神经元同步后呈现簇放电特征或峰放电特征,除与两耦合神经元独自放电模式有关外,还与电突触耦合强度有一定的内在关系.     

G蛋白对神经细胞电压依赖性离子通道的直接调制作用

神经递质对神经细胞电压依赖性离子通道具有调制作用。近年来观察到,神经递质可通过Ｇ蛋白介导对神经细胞电压依赖性离子通道直接发挥调制作用,而不经过目前已知的多种第二信使的介导,使人们对递质,Ｇ蛋白与电压依赖性离子通道间的相互关系有了新的认识。     

乌拉坦对大鼠海马CA1区锥体神经元自发放电的抑制作用

为了深入研究麻醉药乌拉坦对大鼠海马CA1锥体神经元自发放电的作用及其机制,分析了10 mmol/L乌拉坦对自发放电、电压门控钠通道、电压门控钾通道的作用．从自发放电信号中计算了放电频率、提取了峰峰间隔序列(ISI)并利用样品熵和去趋势波动法对ISI进行了非线性分析．结果表明,乌拉坦不仅抑制了自发放电的频率,而且降低了自发放电ISI序列的复杂度并弱化了其长时程相关性．离子通道研究结果表明,乌拉坦显著地抑制了钠通道电流(I_Na),对延迟整流钾通道电流(I_K)和瞬时外向钾通道电流(I_A)虽然也有抑制作用但无统计学意义．由于乌拉坦不影响突触传递,因此它可能通过抑制I_Na使自发放电的阈值升高而降低放电频率,同时,由于参与的通道数量或活性降低而使得ISI的复杂度下降,长时程相关性弱化．     

Izhikevich神经元网络的同步与联想记忆

联想记忆是人脑的一项重要功能。以Izhikevich神经元模型为节点,构建神经网络,神经元之间采用全连结的方式;以神经元群体的时空编码（spatio-temporal coding）理论研究所构建神经网络的联想记忆功能。在加入高斯白噪声的情况下,调节网络中神经元之间的连接强度的大小,当连接强度和噪声强度达到一个阈值时网络中部分神经元同步放电,实现了存储模式的联想记忆与恢复。仿真结果表明,神经元之间的连接强度在联想记忆的过程中发挥了重要的作用,噪声可以促使神经元间的同步放电,有助于神经网络实现存储模式的联想记忆与恢复。     

感觉神经放电中的随机共振现象和噪声的作用

随机共振现象是非线性系统中普遍存在的自然现象 ,其中 ,噪声可以帮助检测弱信号而不是淹没弱信号。本文介绍了感觉神经放电活动中的随机共振现象和产生的机制 ,揭示了神经系统利用噪声检测弱信号的机制 ,并提出了随机共振在神经系统信息处理中的可能作用。     

神经元的稳定性与随机性整数倍放电

在大鼠损伤背根节神经元的自发放电中发现了整数倍放电,为了阐明这种放电所产生的原因,首先研究神经元模型中确定性混沌所引起的整数倍放电与噪声所诱发的整数放电的峰峰间期序列,通过分析得到前者的ＩＳＩ序列是非线性可预报的,具有确定的非线性特性,但由噪声所诱发的整数倍放电的ＩＳＩ序列是不可预报的,这表明这两种机制所产生的整数倍放电具有不同的特点,存在着定笥的差别,并且混沌运动所产生的整数倍放电是由混沌中各种     

黑质致密部神经元的反应性与放电型式的关系

通过研究黑质致密部（subastantia nigra compacta,SNc）神经元的放电型式与其对谷氨酸、多巴胺及缺氧敏感性的关系,探讨“非周期敏感”的现象在神经系统的普通性。在幼鼠的脑片胞外记录SNc神经元的自发放电,比较周期与非周期放电神经元对该三种刺激反应的敏感性,并对非周期放电神经元的动作电位峰峰间期序列（interspike interval,ISI）进行非线性动力学分析。结果表明,非周期放电神经元比周期放电神经元对上述三种缺少更敏感;非周期放电神经元的放电ISI序列含有非稳定周期轨道族,提示非周期放电存在确定的动力学机制。     

神经元的确定性与随机性整数倍放电

在大鼠损伤背根节神经元的自发放电中发现了整数倍放电, 为了阐明这种放电所产生的原因, 首先研究神经元模型中确定性混沌所引起的整数倍放电与噪声所诱发的整数倍放电的峰峰间期（ＩＳＩ） 序列,通过分析得到前者的ＩＳＩ序列是非线性可预报的,具有确定的非线性特性,但由噪声所诱发的整数倍放电的ＩＳＩ序列是不可预报的, 这表明这两种机制所产生的整数倍放电具有不同的特点,存在着定性的差别,并且混沌运动所产生的整数倍放电是由混沌中各阶不稳定周期轨道决定的。从这种差别出发,分析了实验中整数倍放电的ＩＳＩ 序列,得到该ＩＳＩ 序列是可非线性预报的,这表明大鼠损伤背根节神经元自发放电中的整数倍放电更可能是由确定性机制所产生的     

实验性神经起步点自发放电的分叉和整数倍节律

在实验性神经起步点发现了放电峰峰间期序列随细胞外[Ca^2 ]变化产生的加周期分叉和整数倍节律。并用确定性Chay模型和随机Chay模型进行数值模拟。从模拟实验结果的角度看,加周期分叉过程遵从Chay模型决定的确定性机制,随机因素对其有影响但影响较小;而在相应的参数区间,整数倍节律则是在随机因素驱动下产生,是随机共振现象,是由确定性机制和随机因素共同作用的结果。这表明,实验性神经起步点放电节律的分叉和随机共振现象的出现是必然的,受确定性机制和随机因素共同影响。但在不同参数区间,随机因素对神经放电节律的作用不同。     

Intrinsic coherence resonance in excitable membrane patches

The influence of intrinsic channel noise on the spiking activity of excitable membrane patches is studied by use of a stochastic generalization of the Hodgkin-Huxley model. Internal noise stemming from the stochastic dynamics of individual ion channels does affect the electric properties of the cell-membrane patches. There exists an optimal size of the membrane patch for which the internal noise alone can cause a nearly regular spontaneous generation of action potentials. We consider the influence of intrinsic channel noise in presence of a constant and an oscillatory current driving for both, the mean interspike interval and the phenomenon of coherence resonance for neuronal spiking. Given small membrane patches, implying that channel noise dominates the excitable dynamics, we find the phenomenon of intrinsic coherence resonance. In this case, the relatively regular spiking behavior becomes essentially independent of an applied stimulus. We observed, however, the occurrence of a skipping of supra-threshold input events due to channel noise for intermediate patch sizes. This effect consequently reduces the overall coherence of the spiking.     

The selectivity of noise and coupling for coherence biresonance and array-enhanced coherence biresonance in coupled neural systems

The selectivity of noise and coupling for coherence biresonance (CBR) and array-enhanced coherence biresonance (AECBR) in coupled neural systems has been investigated. It is shown that, depending on the coupling strength and noise intensity, various coherence behaviors and phenomena are exhibited, including CBR, coherence resonance without tuning, AECBR and undamped signal transmission. There exist optimal coupling and noise regions for the occurrence of CBR and AECBR in the transmission of noise-induced oscillations (NIOs).     

Synaptic Symmetry Increases Coherence in a Pair of Excitable Electronic Neurons

We study how the synaptic connections in a pair of excitable electronic neurons affect the coherence of their spike trains when the neurons are submitted to noise from independent sources. The coupling is provided by electronic circuits which mimic the dynamics of chemical AMPA synapses. In particular, we show that increasing the strength of an unidirectional synapse leads to a decrease of coherence in the post-synaptic neuron. More interestingly, we show that the decrease of coherence can be reverted if we add a synapse of sufficient strength in the reverse direction. Synaptic symmetry plays an important role in this process and, under the right choice of parameters, increases the network coherence beyond the value achieved at the resonance due to noise alone in uncoupled neurons. We also show that synapses with a longer time scale sharpen the dependency of the coherence on the synaptic symmetry. The results were reproduced by numerical simulations of a pair of synaptically coupled FitzHugh-Nagumo models.     

Improved Detection of Magnetic Signals by a MEMS Sensor Using Stochastic Resonance

We introduce the behavior of the electrical output response of a magnetic field sensor based on microelectromechanical systems (MEMS) technology under different levels of controlled magnetic noise. We explored whether a particular level of magnetic noise applied on the vicinity of the MEMS sensor can improve the detection of subthreshold magnetic fields. We examined the increase in the signal-to-noise ratio (SNR) of such detected magnetic fields as a function of the magnetic noise intensity. The data disclosed an inverted U-like graph between the SNR and the applied magnetic noise. This finding shows that the application of an intermediate level of noise in the environment of a MEMS magnetic field sensor improves its detection capability of subthreshold signals via the stochastic resonance phenomenon.     

Numerical exploration of the influence of neural noise on the psychometric function at low stimulation intensity levels

The relationship between stimulus intensity and the probability of detecting the presence of the stimulus is described by the psychometrical function. The probabilistic nature of this relationship is based on the stochastic behaviour of sensory neural channels and sensory networks involved in perceptual processing (Kiang 1968). This study tries to establish a continuum of variability across different levels of integration in the central nervous system. Once the opening and closing times of ionic channels was simulated, a threshold to the collective behaviour of voltage-gated ionic channels was imposed in order to generate the spike train of a single neuron. Afterwards, the trains of spikes of different neurons were added up, simulating the activity of a sensory nerve. By adding the activity due to the stimulus to the spontaneous neural behaviour, the psychometric function was simulated using a thresholding approach. The results can replicate the stochastic resonance phenomenon, but also open up the possibility that attentional phenomena can be mediated not only by increasing neural activity (bursting or oscillatory), but also by increasing noise at the neural level.     

Spiking regularity in a noisy small-world neuronal network

The regularity of spiking oscillations is studied in the networks with different topological structures. The network is composed of coupled Fitz-Hugh-Nagumo neurons driven by colored noise. The investigation illustrates that the spike train in both the regular and the Watts-Strogatz small-world neuronal networks can show the best regularity at a moderate noise intensity, indicating the existence of coherence resonance. Moreover, the temporal coherence of the spike train in the small-world network is superior to that in a regular network due to the increase of the randomness of the network topology. Besides the noise intensity, the spiking regularity can be optimized by tuning the randomness of the network topological structure or by tuning the correlation time of the colored noise. In particular, under the cooperation of the small-world topology and the correlation time, the spike train with good regularity could sustain a large magnitude of the local noise.     

Periodic coupling strength-dependent multiple coherence resonance by time delay in Newman–Watts neuronal networks

Recently, multiple coherence resonance induced by time delay has been observed in neuronal networks with constant coupling strength. In this paper, by employing Newman–Watts Hodgkin–Huxley neuron networks with time-periodic coupling strength, we study how the temporal coherence of spiking behavior and coherence resonance by time delay change when the frequency of periodic coupling strength is varied. It is found that delay induced coherence resonance is dependent on periodic coupling strength and increases when the frequency of periodic coupling strength increases. Periodic coupling strength can also induce multiple coherence resonance, and the coherence resonance occurs when the frequency of periodic coupling strength is approximately multiple of the spiking frequency. These results show that for periodic coupling strength time delay can more frequently optimize the temporal coherence of spiking activity, and periodic coupling strength can repetitively optimize the temporal coherence of spiking activity as well. Frequency locking may be the mechanism for multiple coherence resonance induced by periodic coupling strength. These findings imply that periodic coupling strength is more efficient for enhancing the temporal coherence of spiking activity of neuronal networks, and thus it could play a more important role in improving the time precision of information processing and transmission in neural networks.     

Presynaptic N-type calcium channels regulate synaptic growth

Voltage-gated calcium channels couple changes in membrane potential to neuronal functions regulated by calcium, including neurotransmitter release. Here we report that presynaptic N-type calcium channels not only control neurotransmitter release but also regulate synaptic growth at Drosophila neuromuscular junctions. In a screen for behavioral mutants that disrupt synaptic transmission, an allele of the N-type calcium channel locus (Dmca1A) was identified that caused synaptic undergrowth. The underlying molecular defect was identified as a neutralization of a charged residue in the third S4 voltage sensor. RNA interference reduction of N-type calcium channel expression also reduced synaptic growth. Hypomorphic mutations in syntaxin-1A or n-synaptobrevin, which also disrupt neurotransmitter release, did not affect synapse proliferation at the neuromuscular junction, suggesting calcium entry through presynaptic N-type calcium channels, not neurotransmitter release per se, is important for synaptic growth. The reduced synapse proliferation in Dmca1A mutants is not due to increased synapse retraction but instead reflects a role for calcium influx in synaptic growth mechanisms. These results suggest N-type channels participate in synaptic growth through signaling pathways that are distinct from those that mediate neurotransmitter release. Linking presynaptic voltage-gated calcium entry to downstream calcium-sensitive synaptic growth regulators provides an efficient activity-dependent mechanism for modifying synaptic strength.     

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction. Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.     

Signal/noise analysis of FRET-based sensors

Molecular sensors based on intramolecular Förster resonance energy transfer (FRET) have become versatile tools to monitor regulatory molecules in living tissue. However, their use is often compromised by low signal strength and excessive noise. We analyzed signal/noise (SNR) aspects of spectral FRET analysis methods, with the following conclusions: The most commonly used method (measurement of the emission ratio after a single short wavelength excitation) is optimal in terms of signal/noise, if only relative changes of this uncalibrated ratio are of interest. In the case that quantitative data on FRET efficiencies are required, these can be calculated from the emission ratio and some calibration parameters, but at reduced SNR. Lux-FRET, a recently described method for spectral analysis of FRET data, allows one to do so in three different ways, each based on a ratio of two out of three measured fluorescence signals (the donor and acceptor signal during a short-wavelength excitation and the acceptor signal during long wavelength excitation). Lux-FRET also allows for calculation of the total abundance of donor and acceptor fluorophores. The SNR for all these quantities is lower than that of the plain emission ratio due to unfavorable error propagation. However, if ligand concentration is calculated either from lux-FRET values or else, after its calibration, from the emission ratio, SNR for both analysis modes is very similar. Likewise, SNR values are similar, if the noise of these quantities is related to the expected dynamic range. We demonstrate these relationships based on data from an Epac-based cAMP sensor and discuss how the SNR changes with the FRET efficiency and the number of photons collected.