非洲爪蟾顶盖神经元的抑制性微突触后电流频率和振幅的电压依赖性

采用全细胞记录膜片钳技术,研究非洲爪蟾脑片视顶盖神经元微突触后电流（miniature inhibitory postsynaptic current,mIPSC）频率和振幅对电压依赖关系。观察到以下结果：（1）当通过改变记录电极内的DC电流,将神经元的膜电位从静息电位逐步（每步10mV的增量）去极化或超极化时,mIPSC的频率和,或振幅分别升高或降低。随着膜电位的去极化,mIPSC的频率逐渐升高;当钳位电压升至＋10mV时,mIPSC的频率达到最高值。（2）当神经元去极化时,振幅仅轻微升高。膜电位去极化达到-30mV或-40mV时,mIPSC的振幅最大：进一步去极化,振幅反而下降。另外,在膜电位去极化至-20mV和＋10mV之间时,可记录到大的mIPSC。（3）在无Ca^2＋浸浴液中,mIPSC的频率和振幅也随膜电位的去极化而逐步增高,但频率的增高幅度远不如在生理盐水浸浴中增高幅度明显。（4）当浸浴液中[K＋]0增高时,mIPSC的频率明显降低,而振幅轻微降低。当细胞外[K^＋]。浓度升高超过20mmol／L时,神经元产生明显的缓慢内向或外向膜电流。mIPSC频率和振幅与膜电位存在依赖性的可能机制在文中作了简短的讨论。     

小鼠下丘听神经元的反应潜伏期对特征频率的表达

频率是声音的基本参数之一. 听觉神经元对声音频率的反应可以表现为放电率和反应潜伏期的变化. 大部分神经元放电率随频率的改变呈多种变化, 而神经元对声音反应的放电潜伏期往往比较稳定, 提示潜伏期能有效地表达频率信息. 本文研究了BALB/C小鼠下丘听神经元对纯音频率反应的放电率及潜伏期特性, 实验结果表明: 神经元对特征频率的反应潜伏期通常最短, 随声强的变化改变不大; 而神经元对纯音频率反应的放电率随频率改变呈多种变化, 尤其当声强增强时. 实验结果提示小鼠下丘神经元的反应潜伏期具有较放电率更准确表征特征频率的特性.     

交流外电场下映射神经元放电节律的分析

神经元不同的放电节律承载着不同的刺激信息。文章基于神经元映射模型,研究低频交流电场对神经元放电节律的影响。在外部刺激下映射模型表现出丰富的放电模式,包括周期簇放电、周期峰放电、交替放电和混沌放电。神经元对刺激频率和振幅的变化极为敏感,随着频率的增大,放电节律表现出从簇放电到峰放电和混沌放电的反向加周期分岔序列;在周期节律转迁过程中存在一种新的交替节律,其放电序列为两种周期放电模式的交替,峰峰间期序列具有整数倍特征。外电场的频率影响细胞内、外离子振荡周期,导致神经元放电与刺激信号同步,对放电节律的影响更为明显。研究结果揭示了交流外电场对神经元放电节律的作用规律,有助于探寻外电场对生物神经系统兴奋性的影响和神经系统疾病的致病机理。     

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.     

大蹄蝠多普勒正负补偿效应的声波特征与比较

利用单摆装置模拟大蹄蝠的飞行状态并实时记录其回声定位信号,以研究其多普勒频移补偿行为。与其静息状态下的超声波特征比较,发现大蹄蝠在接近目标的过程中有多普勒正补偿效应:叫声频率随相对速度改变而成正相关变化;当相对速度最大时,其叫声频率相对静息状态频率降低最多,而相对速度为零时,叫声频率回复到静息时频率。而当大蹄蝠远离目标时,有多普勒负补偿效应:叫声频率随相对速度改变成正相关变化,叫声频率在相对速度最大时,升高最多,但相同速度下升高之值较正补偿值低得多。另外,负补偿效应出现的频率较正补偿值低,这可能是由蝙蝠生理结构的限制以及自然状态下罕见的多普勒负补偿条件所决定。     

藜芦碱和乌头碱在受损背根节神经元诱发不同的放电模式

为了研究钠通道失活门阻断后受损背根节神经元放电模式的变化特征,在大鼠背根节慢性压迫模型上采用单纤维技术记录A类神经元的自发放电。藜芦碱和乌头碱是钠通道失活门的抑制剂,但二者作用于不同的位点,前者结合于D2-S6,后者结合于D3-S6。我们比较了这两种试剂引发的放电模式。结果发现,在同一神经元,藜芦碱(1．5～5．0μmol／L)可以引起放电峰峰间期的慢波振荡,即峰峰间期由大逐渐减小,然后又逐渐增大,形成重复的振荡波形,每个振荡持续约数十秒至数分钟：而乌头碱(10～200μmol／L)则引起强直性放电,即峰峰间期逐渐减小,然后维持在一个稳定的水平。这两种不同的放电模式不因背景放电或试剂浓度的不同而发生明显的改变。实验结果表明,藜芦碱和乌头碱在受损的背根节神经元可以引发不同的放电模式,这可能与它们结合于钠通道上不同位点的抑制作用有关。     

基于混沌降噪的神经元放电峰峰间期序列分析

围绕如何来消除神经元峰峰间期序列中随机噪声影响从而提取出决定不规则性的确定性动力学关系这个问题,本文首先简要介绍峰峰间期序列样本的制备,然后着重讨论一个简单可行的混沌时间序列降噪方法的原理和算法实现,最终将该方法运用到神经元放电活动数值模拟和实验记录到的峰峰间期时间序列样本分析中。本文分析结果再次证明神经放电活动中确实存在着不规则混沌运动,而且降噪结果进一步揭示了神经电生理实验中决定混沌放电的不连续但分段光滑的单峰函数关系     

豚鼠背侧海马锥体细胞自发放电特征

实验采用细胞外玻璃微电极采集豚鼠海马神经元放电信号,并将信号转化为峰峰间期(interspike interval,ISI)以研究麻醉和清醒状态海马锥体细胞自发放电线性和非线性特点。实验建立了豚鼠海马锥体细胞与中间神经元电生理鉴别标准;麻醉和清醒状态下豚鼠海马CA1和CA3区锥体细胞自发放电频率、时程、复杂度等无显著区别;麻醉组豚鼠海马锥体细胞ISI序列的复杂度小于清醒组,锥体细胞分型和ISI变异度等表现不同。实验表明,麻醉和清醒状态下豚鼠海马锥体细胞自发放电呈不同线性和非线性特征。传统和非线性研究手段的结合,可能较全面地反映海马锥体细胞自发放电特性。     

大鼠脊髓背角神经元痛放电确定性行为的年龄相关变化

为阐明脊髓背角神经元痛放电的年龄相关的动力学变化,本研究采用非线性预报方法,对两组不同年龄大鼠(成年青龄鼠3～4月龄,老年鼠＞22月龄)组织损伤诱发的脊髓背角神经元痛放电峰峰间期序列进行了确定性行为的定量分析.结果显示,皮下注入蜜蜂毒,在两组大鼠均诱发脊髓背角广动力域神经元长时程放电,而老龄大鼠的痛放电峰峰间期序列表现出更高的可确定性.本研究表明,单个神经元的痛放电动力学在整个生命过程中并不是恒定不变的,伤害性神经元活动的年龄相关动力学变化可能是老年人群中多样化痛反应的内在机制之一.     

弱背景噪声对大鼠听皮层神经元频率调谐的影响

在自然环境中,人和动物常在一定的背景噪声下感知信号声刺激,然而,关于低强度的弱背景噪声如何影响听皮层神经元对声刺激频率的编码尚不清楚.本研究以大鼠听皮层神经元的频率反应域为研究对象,测定了阈下背景噪声对79个神经元频率反应域的影响.结果表明,弱背景噪声对大鼠初级听皮层神经元的听反应既有抑制性影响、又有易化性影响.一般来说,抑制性影响使神经元的频率调谐范围和最佳频率反应域缩小,易化性影响使神经元的频率调谐范围和最佳频率反应域增大.对于少数神经元,弱背景噪声并未显著改变其频率调谐范围,但却改变了其最佳频率反应域范围.弱背景噪声对63.64%神经元的特征频率和55.84%神经元的最低阈值无显著影响.神经元频率调谐曲线的尖部比中部更容易受到弱背景噪声的影响.该研究结果有助于我们进一步理解复杂声环境下大脑听皮层对听觉信息的编码机制.     

Sensitive dependence of the coefficient of variation of interspike intervals on the lower boundary of membrane potential for the leaky integrate-and-fire neuron model

After the report of Softky and Koch [Softky, W.R., Koch, C., 1993. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334-350], leaky integrate-and-fire models have been investigated to explain high coefficient of variation (CV) of interspike intervals (ISIs) at high firing rates observed in the cortex. The purpose of this paper is to study the effect of the position of a lower boundary of membrane potential on the possible value of CV of ISIs based on the diffusional leaky integrate-and-fire models with and without reversal potentials. Our result shows that the irregularity of ISIs for the diffusional leaky integrate-and-fire neuron significantly changes by imposing a lower boundary of membrane potential, which suggests the importance of the position of the lower boundary as well as that of the firing threshold when we study the statistical properties of leaky integrate-and-fire neuron models. It is worth pointing out that the mean-CV plot of ISIs for the diffusional leaky integrate-and-fire neuron with reversal potentials shows a close similarity to the experimental result obtained in Softky and Koch [Softky, W.R., Koch, C., 1993. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334-350].     

Interspike interval statistics in the Ornstein-Uhlenbeck neuronal model with signal-dependent noise

The stochastic leaky integrate-and-fire (LIF) continuous model is studied under the condition that the amplitude of noise is a function of the input signal. The coefficient of variation (CV) of interspike intervals (ISIs) is investigated for different types of dependencies between the noise and the signal. Finally, we present the CV and the ISI density resulting from the special choice of parameters of the input that gave rise to a contra-intuitive behavior of the transfer function in Lánsky and Sacerdote [Phys. Lett. A 285 (2001) 132].     

Swim pacemakers in box jellyfish are modulated by the visual input

A major part of the cubozoan central nervous system is situated in the eye-bearing rhopalia. One of the neuronal output channels from the rhopalia carries a swim pacemaker signal, which has a one-to-one relation with the swim contractions of the bell shaped body. Given the advanced visual system of box jellyfish and that the pacemaker signal originates in the vicinity of these eyes, it seems logical to assume that the pacemakers are modified by the visual input. Here, the firing frequency and distribution of inter-signal intervals (ISIs) of single pacemakers are examined in the Caribbean box jellyfish, Tripedalia cystophora. It is shown that the absolute ambient light intensity, if kept constant, has no influence on the signal, but if the intensity changes, it has a major impact on both frequency and ISIs. If the intensity suddenly drops there is an increase in firing frequency, and the ISIs become more homogeneously distributed. A rise in intensity, on the other hand, produces a steep decline in the frequency and makes the ISIs highly variable. These electrophysiological data are correlated with behavioral observations from the natural habitat of the medusae.     

A joint interspike interval difference stochastic spike train analysis: detecting local trends in the temporal firing patterns of single neurons

We introduce a stochastic spike train analysis method called joint interspike interval difference (JISID) analysis. By design, this method detects changes in firing interspike intervals (ISIs), called local trends, within a 4-spike pattern in a spike train. This analysis classifies 4-spike patterns that have similar incremental changes. It characterizes the higher-order serial dependence in spike firing relative to changes in the firing history. Mathematically, this spike train analysis describes the statistical joint distribution of consecutive changes in ISIs, from which the serial dependence of the changes in higher-order intervals can be determined. It is similar to the joint interspike interval (JISI) analysis, except that the joint distribution of consecutive ISI differences (ISIDs) is quantified. The graphical location of points in the JISID scatter plot reveals the local trends in firing (i.e., monotonically increasing, monotonically decreasing, or transitional firing). The trajectory of these points in the serial-JISID plot traces the time evolution of these trends represented by a 5-spike pattern, while points in the JISID scatter plot represent trends of a 4-spike pattern. We provide complete theoretical interpretations of the JISID analysis. We also demonstrate that this method indeed identifies firing trends in both simulated spike trains and spike trains recorded from cultured neurons. Received: 13 May 1997 / Accepted in revised form: 9 December 1998     

Effects of afterhyperpolarization on neuronal firing.

Stein's model for a neuron is studied. This model is modified to take into account the effects of afterhyperpolarization on the neuronal firing. The relative refractory phase, following the absolute one, is modelled by a time-increasing amplitude of postsynaptic potentials and it is also incorporated into the model. Besides the simulation of the model, some theoretical results and approximation methods are derived. Afterhyperpolarization tends to preserve the linearity of the frequency transfer characteristic and it has a limited effect on the moments of the interspike intervals in general. The main effects are seen at high firing rates and in the removal of short intervals in the interspike interval histogram.     

Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons

The spike trains that transmit information between neurons are stochastic. We used the theory of random point processes and simulation methods to investigate the influence of temporal correlation of synaptic input current on firing statistics. The theory accounts for two sources for temporal correlation: synchrony between spikes in presynaptic input trains and the unitary synaptic current time course. Simulations show that slow temporal correlation of synaptic input leads to high variability in firing. In a leaky integrate-and-fire neuron model with spike afterhyperpolarization the theory accurately predicts the firing rate when the spike threshold is higher than two standard deviations of the membrane potential fluctuations. For lower thresholds the spike afterhyperpolarization reduces the firing rate below the theory's predicted level when the synaptic correlation decays rapidly. If the synaptic correlation decays slower than the spike afterhyperpolarization, spike bursts can occur during single broad peaks of input fluctuations, increasing the firing rate over the prediction. Spike bursts lead to a coefficient of variation for the interspike intervals that can exceed one, suggesting an explanation of high coefficient of variation for interspike intervals observed in vivo.     

The effects of recurrent inhibitory feedback in shaping discharge patterns of motoneurones excited by phasic muscle stretches

The spinal motoneurone-Renshaw cell circuitry, which constitutes an intricate negative feedback system, was investigated with respect to its significance for the shaping of motoneurone firing patterns excited by strong phasic inputs. Discharge patterns of single motoneurones were compared before and after opening of the recurrent inhibitory pathways by Renshaw cell blocking agents. Serial correlograms computed from motoneurone interspike intervals which were modulated by sinusoidal muscle stretch replicated the input periodicity, but were not changed in any consistent manner after Renshaw cell blockage. Longterm regularity and periodicity of motoneurone firing, i.e. the overall response characteristics, do not appear to be significantly determined by Renshaw cells under these conditions. The modulation of motoneurone interspike intervals was assessed by computing power spectra for corresponding instantaneous frequencies. The harmonic contents (2^nd and 3^rd harmonic of the driving frequency) of these spectra tended to decrease after Renshaw cell depression. The distortion of signal transmission in a single motoneurone channel is thus stronger with, than without, the recurrent inhibitory feedback. The implication of these findings for signal transmission from the spinal cord to the muscle is discussed.     

Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons

This paper examines how noise interacts with the non-linear dynamical mechanisms of neuronal stimulus. We study the spike trains generated by a minimal Hodgkin-Huxley type model of a cold receptor neuron. The distributions of interspike intervals(ISIs) of purely deterministic simulations exhibit considerable differences compared to the noisy ones. We quantify the effect of noise using ISI return plots and the ISI-distance recently proposed by Kreuz et al. (J Neurosci Meth, 165:151–161, 2007). It is shown that the spike trains of a cold receptor neuron are more strongly affected by noise for low temperatures than for high temperatures. This trend is also observed in both regimes of cold receptors: tonic firing(which occurs for low and high temperatures) and bursting (which occurs for intermediate temperatures).     

Predicting the Responses of Repetitively Firing Neurons to Current Noise

We used phase resetting methods to predict firing patterns of rat subthalamic nucleus (STN) neurons when their rhythmic firing was densely perturbed by noise. We applied sequences of contiguous brief (0.5–2 ms) current pulses with amplitudes drawn from a Gaussian distribution (10–100 pA standard deviation) to autonomously firing STN neurons in slices. Current noise sequences increased the variability of spike times with little or no effect on the average firing rate. We measured the infinitesimal phase resetting curve (PRC) for each neuron using a noise-based method. A phase model consisting of only a firing rate and PRC was very accurate at predicting spike timing, accounting for more than 80% of spike time variance and reliably reproducing the spike-to-spike pattern of irregular firing. An approximation for the evolution of phase was used to predict the effect of firing rate and noise parameters on spike timing variability. It quantitatively predicted changes in variability of interspike intervals with variation in noise amplitude, pulse duration and firing rate over the normal range of STN spontaneous rates. When constant current was used to drive the cells to higher rates, the PRC was altered in size and shape and accurate predictions of the effects of noise relied on incorporating these changes into the prediction. Application of rate-neutral changes in conductance showed that changes in PRC shape arise from conductance changes known to accompany rate increases in STN neurons, rather than the rate increases themselves. Our results show that firing patterns of densely perturbed oscillators cannot readily be distinguished from those of neurons randomly excited to fire from the rest state. The spike timing of repetitively firing neurons may be quantitatively predicted from the input and their PRCs, even when they are so densely perturbed that they no longer fire rhythmically.