神经元放电阈值的可变性及其意义

神经元能够将不同时空模式的突触输入转化为时序精确的动作电位输出，这种灵活、可靠的信息编码方式是神经集群在动态环境或特定任务下产生所需活动模式的重要基础。动作电位的产生遵循全或无规律，只有当细胞膜电压达到放电阈值时，神经元才产生动作电位。放电阈值在细胞内和细胞间具有高度可变性，具体动态依赖于刺激输入和放电历史。特别是，放电阈值对动作电位起始前的膜电压变化十分敏感，这种状态依赖性产生的生物物理根源包括Na⁺失活和K⁺激活。在绝大多数神经元中，动作电位的触发位置是轴突起始端，这个位置处的阈值可变性是决定神经元对时空输入转化规律的关键因素。但是，电生理实验中动作电位的记录位置却通常是胞体或近端树突，此处的阈值可变性高于轴突起始端，而其产生的重要根源是轴突动作电位的反向传播。基于胞体测量的相关研究显示，放电阈值动态能够增强神经元的时间编码、特征选择、增益调控和同时侦测能力。本文首先介绍放电阈值的概念及量化方法，然后详细梳理近年来国内外关于放电阈值可变性及产生根源的研究进展，在此基础上归纳总结放电阈值可变性对神经元编码的重要性，最后对未来放电阈值的研究方向进行展望。     

视网膜神经节细胞对自然刺激的时空反应模式

神经系统信息处理的理论研究和计算结果表明,视皮层可以通过稀疏编码 (sparse coding) 模式来处理自然刺激信息.神经元群体中,单个神经元在大多数时间里没有强的脉冲发放 (时间维稀疏性,lifetime sparseness),而针对某一刺激,只有少数神经元在特定的时间内发放 (空间维稀疏性,population sparseness).从神经元放电的时间和空间模式两个方面考察了视网膜神经节细胞群体对自然刺激(电影)的编码方式,并同实验室常用的伪随机棋盘格刺激下视网膜的反应模式进行比较,分析了视网膜神经节细胞反应的稀疏性指标,并深入探讨了其内在的时间和空间特点.结果提示,视觉系统在其最初阶段——视网膜——即开始采用一种高效节能的稀疏编码方式来处理自然视觉信息,单个神经元的时间维稀疏性节省了代谢能量消耗,而群体神经元中邻近神经元的动态成组协同发放,提高了信息向突触后神经元传递的有效性.     

神经元信息编码的代谢消耗：动作电位与能量效率

人脑是一个高效、可靠的信息处理系统,它主导着个体的认知、情感、意识与行为,这些功能的实现需要不断地消耗代谢能量.大脑的能量需求主要被神经元信息编码所消耗,相应的亚细胞过程包括产生和传导动作电位、维持静息电位以及突触传递.神经元编码信息的主要载体是动作电位序列,它的产生与传导贡献了大脑的大部分代谢消耗.动作电位的能量消耗受离子通道的生物物理特性控制.生物物理特性的细胞特异性和空间异质性使得动作电位对代谢能量的利用效率呈现高度可变性,它为理解神经元代谢消耗的规律、起因与结果带来了挑战.本文首先介绍参与神经元编码的亚细胞过程及它们在大脑和小脑皮层中的代谢消耗,然后详细梳理近年来关于动作电位代谢消耗的研究成果,重点讨论影响其能量效率的生物物理因素和放电形状特性,并归纳总结放电消耗的特点,最后对未来神经元编码的代谢消耗研究进行展望.     

变间隔的脉冲改变高频刺激对于脑神经元的作用

通常采用恒定电脉冲间隔的高频刺激(high-frequency stimulation,HFS),进行深部脑刺激治疗帕金森氏症等运动障碍疾病.为了开发适用于不同脑疾病治疗的新刺激模式,近年来脉冲间隔(inter-pulse-interval,IPI)变化的变频刺激模式受到关注.已有研究表明,即使具有相同的平均电脉冲频率,变频刺激与恒频刺激的治疗效果也不同.我们推测,变频刺激的短小IPI变化就足以改变HFS对于神经元的作用.为了验证此推测,本文在大鼠海马CA1区锥体神经元的输入轴突纤维上交替施加恒频刺激(100或133 Hz,即IPI=10 ms或7.5 ms)和随机变频刺激(100～200 Hz,即IPI=5～10 ms,平均频率为133 Hz),记录并分析刺激下游神经元群体的诱发电位,用于定量评价神经元对于恒频和变频刺激的响应.实验结果表明,持续的恒频刺激使得神经元的响应从最初的同步发放形成的群峰电位(population spike,PS)转变为非同步的动作电位发放(即单元锋电位).但是,当刺激切换为变频模式时,却又可以诱发神经元群体同步产生动作电位,重新形成PS波.并且,变频刺激诱发的PS幅值和神经元发放的同步程度可达基线的单脉冲刺激诱发波的水平.但是,PS的发生率只有脉冲刺激频率的7%左右,表明在持续的变频刺激时,多个脉冲累积的作用才能诱发这种同步的神经元发放.而且PS的出现与前导IPI的长度之间存在一定关系.神经元的轴突和突触等结构对于高频刺激的非线性响应可能是变频刺激诱发同步活动的原因.这些结果表明,变频刺激序列中短小的间隔变化可以产生与恒定间隔不同的调控作用.本文的结果对于揭示脑刺激的作用机制,促进新型刺激模式的开发及其在不同类型脑疾病治疗中的应用具有重要意义.     

神经信息学的原理与展望

神经信息学是研究神经系统信息的载体形式,神经信息的产生、传输、加工、编码、存储与提取机理,以及建立神经数据库系统的科学。它是脑科学,信息科学和计算机科学相互交叉的边缘学科。神经信息学可分为分子神经信息学和系统神经信息学两个层次。神经信息编码可分为神经元脉冲序列的数字编码和突触联结权重编码两种编码方式。对21世纪神经信息学可能取得的新进展进行分析和预测,并论证开展人类神经组计划（HuNP）和建立神经数据库系统的必要性与可行性。对人类神经计划与人类脑计划的异同步,进行比较和讨论。     

灭多威对美洲大蠊腹六神经节突触传递的阻断作用

用电生理学方法研究了灭多威对美洲大蠊Periplanetaamerwana腹六神经节(A6节)突触传递的影响。用灭多威溶液浸泡A6节,电刺激尾须神经粗支,用甘露醇间隙法记录兴奋性突触后电位(EPSP)和突触后动作电位。给予弱刺激只记录到EPSP时,灭多威作用初期EPSP幅度增加、时程延长,能诱发突触后动作电位,随后EPSP逐渐减小至消失,冲洗可恢复,突触前反应保持不变。增加电刺激强度记录到突触后动作电位时,灭多威可阻断A6节的突触传递,阻断时间是浓度依赖性的,阻断是可逆的,但冲洗30 min仍保留一定的后作用。对美洲大蠊雄性成虫腹腔注射灭多威测定致死中量(LD₅₀)为(3.56±0.01) μg/g体重。根据灭多威的作用机理对其阻断A6节突触传递的特点以及对虫体的毒杀机制进行了讨论。     

利用嗜神经病毒跨突触追踪神经网络研究进展

解析大脑神经网络的连接图谱是认识大脑功能的前提。发展追踪大脑神经环路结构的技术,已成为神经科学研究中的迫切需求。基于嗜神经病毒发展而来的跨突触追踪技术,是揭示大脑神经网络结构的最有效手段,也是神经科学研究中发展十分迅速的领域。不同的嗜神经病毒类型或毒株,都有其独特的分子生物学特性、跨突触标记特性、改造方式。通过使用遗传重组改造的嗜神经病毒追踪神经环路,可以获得特定区域或特定类型神经元多级输出网络、输入网络及单级输入或输出网络。主要介绍神经科学研究中常用的神经病毒及相关的辅助工具病毒特性,及嗜神经病毒介导的各种神经回路标记技术。     

注意机制在嗅球学习过程中的作用

神经生物实验表明, 气味信息在嗅球中是多通道并行处理的, 并且是可学习的, 学习结果依赖于学习时的认知环境. 对于不同类型的突触, 其突触前脉冲作用于突触后的有效时间不同. 按嗅球的生理结构, 构建了一个嗅球模型, 其中神经元间的不同类型的突触连接具有不同的脉冲有效时间. 模拟结果表明这一模型实现了嗅球中信息的多通道处理. 在此基础上, 进一步研究了认知环境对气味学习的影响, 并以不同的反馈频率来表征不同的注意状态. 为满足嗅球中多通道信息编码方式对学习律的要求, 提出了一个兼顾脉冲定时和平均发放速率的反对称的Hebb学习律. 模拟结果表明, 气味在嗅球中的敏感化和习惯化, 可能是在统一的学习律指导下的学习在不同注意状态下产生的结果.     

杀虫环对黑胸大蠊神经突触传递的阻遏作用

用电生理糖间隙法研究杀虫环对黑胸大蠊神经突触传递的作用,并以α-银环蛇毒素作比较。结果证明:1)杀虫环阈浓度1×10^-5M即显著地抑制兴奋性突触后电位(EPSP)。作用开始使之阈值递增,此时只有增加刺激强度方可诱出EPSP。2)(虫非)蠊第Ⅵ腹神经节是胆碱能的。已知突触后阻遏剂如α-银环蛇毒素的作用是N型乙酰胆碱受体(n-AchR)的专一性配基,与杀虫环阻遏神经突触的传递颇为相似,二者均不影响突触后神经元的静息电位和动作电位的传导;而杀虫环对非胆磁能的神经肌肉接头则无影响。3)自发突触后电位随杀虫环处理时间的不同而变化。开始自发释放电位的振幅、频率逐渐增加,继之产生持续期较长的阵发性高频发放,以后又逐渐消失。     

神经解剖学和神经生理学中的基本概念简述(二)

神经冲动动作电位和分级电位用最一般的话来说,神经元的机能是迅速传播和处理信息。神经元传播信息的方法有两种:神经冲动和分级电位。动作电位是神经冲动的电信号,是神经细胞膜电位的一种迅速变化,这种变化能有100毫伏的升降。一次动作电位包括高而短的锋电位和锋电位后面低而长的后电位。一般所说的动作电位实     

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.     

Variability and coding efficiency of noisy neural spike encoders

Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders--an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na(+) and K(+) channels obeying Hodgkin-Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones.     

Encoding of luminance and contrast by linear and nonlinear synapses in the retina

Understanding how neural circuits transmit information is technically challenging because the neural code is contained in the activity of large numbers of neurons and synapses. Here, we use genetically encoded reporters to image synaptic transmission across a population of sensory neurons-bipolar cells in the retina of live zebrafish. We demonstrate that the luminance sensitivities of these synapses varies over 10(4) with a log-normal distribution. About half the synapses made by ON and OFF cells alter their polarity of transmission as a function of luminance to generate a triphasic tuning curve with distinct maxima and minima. These nonlinear synapses signal temporal contrast with greater sensitivity than linear ones. Triphasic tuning curves increase the dynamic range over which bipolar cells signal light and improve the efficiency with which luminance information is transmitted. The most efficient synapses signaled luminance using just 1 synaptic vesicle per second per distinguishable gray level.     

Auditory information coding by modeled cochlear nucleus neurons

In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.     

Recurrent Network Models for Perfect Temporal Integration of Fluctuating Correlated Inputs

Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.     

Spike train timing-dependent associative modification of hippocampal CA3 recurrent synapses by mossy fibers

In the CA3 region of the hippocampus, extensive recurrent associational/commissural (A/C) connections made by pyramidal cells may function as a network for associative memory storage and recall. We here report that long-term potentiation (LTP) at the A/C synapses can be induced by association of brief spike trains in mossy fibers (MFs) from the dentate gyrus and A/C fibers. This LTP not only required substantial overlap between spike trains in MFs and A/C fibers, but also depended on the temporal order of these spike trains in a manner not predicted by the well-known rule of spike timing-dependent plasticity and requiring activation of type 1 metabotropic glutamate receptors. Importantly, spike trains in a putative single MF input provided effective postsynaptic activity for the induction of LTP at A/C synapses. Thus, the timing of spike trains in individual MFs may code information that is crucial for the associative modification of CA3 recurrent synapses.     

Spectral analysis of input spike trains by spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.     

The chronotron: a neuron that learns to fire temporally precise spike patterns

In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

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STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

The spike trains generated by a neuron model are studied by the methods of nonlinear time series analysis. The results show that the spike trains are chaotic. To investigate effect of noise on transmission of chaotic spike trains, this chaotic spike trains are used as a discrete subthreshold input signal to the integrate-and-fire neuronal model and the FitzHugh-Nagumo(FHN) neuronal model working in noisy environment. The mutual information between the input spike trains and the output spike trains is calculated, the result shows that the transformation of information encoded by the chaotic spike trains is optimized by some level of noise, and stochastic resonance(SR) measured by mutual information is a property available for neurons to transmit chaotic spike trains.