反映时序特征的听神经自发发放模型

听神经发放时序在听信息编码中起重要作用,要认识和运用听觉系统对听神经发放时序特征的利用机制必须对听神经发放时序特征有认识,但是已有的随机过程数学模型均不能很好地反映听神经发放时序特征。而关于发放的生理模型,如Hodgkin-Huxley方程、Meddis模型对发放过程的描述均是决定性的、连续性的,不能反映生理过程的离散性和发放过程的随机性。本文基于神经发放生理过程,通过对神经发放条件以及自发发放时神经递质释放时序的描述,建立了一个简单自发发放模型。模型在很好地反映听神经自发发放时序外部统计特征的同时,所得到的神经递质的释放速率在生理上也是合理的。模型还能对纯音激励时听神经的锁相发放做出定性解释,可作为进一步研究反映时序特征的有激励发放模型的基础。     

基于广义线性模型的针刺足三里脊髓背根神经活动解码

神经系统以时空编码形式刻画外部刺激信息, 针刺作为对穴位的机械作用可以等效为对神经系统的一种外部刺激. 为了揭示神经系统如何表达和传递针刺作用, 本文设计了不同频率的针刺动物实验, 即在针刺大鼠足三里穴位时获取脊髓背根神经节电信号. 首先, 经过数据预处理获得单神经元动作电位序列并转化为点过程序列. 其次, 应用广义线性模型(GLM)编码针刺作用, 产生模拟的神经放电序列. 另外, 在模型基础上应用贝叶斯解码, 根据神经放电序列重构针刺随时间变化的位移波形. 最后, 基于时间重标度理论应用分位数分位数(Q-Q)图方法检验编码模型与点过程数据的一致性. 结果表明, GLM能够模拟针刺神经编码, 并正确解码针刺信息. 本文为针刺研究提供了新的视角, 对于构建神经系统与机器接口以改善针刺的临床研究具有潜在意义. 关键词： 针刺 点过程 广义线性模型 神经解码     

针刺足三里的脊髓背根神经电信号非线性特征提取

神经系统在外部刺激下会呈现出丰富的放电模式,针刺作为对穴位的机械作用可以等效为对神经系统的一种外部刺激,在针刺刺激下神经系统会产生不规则的电信号.由于神经系统是高度复杂的非线性动力学系统,神经电信号具有很强的非线性,所以本文设计了提插补法、提插泻法、捻转补法、捻转泻法等四种针刺手法的动物实验,获取针刺大鼠足三里穴位的脊髓背根神经束动作电位序列.采用峰峰间期的思想,应用非线性时间序列的方法分析此动作电位序列,通过计算Lyapunov指数、关联维数以及Lempel-Ziv复杂度等参数,提取神经电信号的非线性特征,得到不同针刺手法的神经电信息的编码;并证明了针刺作用下脊髓背根的神经电信号具有明显的混沌特性. 关键词： 针刺 神经电信号 非线性分析 混沌     

准周期随机声脉冲序列信号在浅海波导中的传播

准周期随机声脉冲序列作为船舶辐射噪声信号模型具有合理性和一定普适性,这里将此模型与海洋波导结合,探讨基于此模型产生的噪声线谱经波导传播后的物理规律变化,即船舶辐射噪声经波导作用后的信号物理特性。理论分析和传播仿真计算表明,准周期声脉冲序列船舶噪声信号所含有的线谱由于受随机波导的随机性和多途干涉共同作用,序列脉冲出现的周期性显著被削弱,线谱相对幅度将"额外"快速衰减,从一个视角揭示了船舶辐射噪声线谱不稳定的原因,为船舶辐射噪声(尤其线谱)特性进一步研究提供了相应的理论支持。      

基于相空间重构理论的舰船辐射噪声非线性特性研究

以相空间重构理论为基础,用TAKENS延时法对时序序列进行相空间重构,在超维相空间中研究舰船辐射噪声的非线性特性,利用相似序列计算出空间轨迹点与其自身的重复度（RPT）参数,绘制了舰船辐射噪声重复度曲线并分析其非线性特性。结果表明,在超维相空间中,舰船辐射噪声表现出具有界于随机的高斯白噪声和确定性的LORENZ吸引子之间的空间几何特性。并且同类目标之间具有相似性,不同类目标之间具有可分性,本文所提出的方法为水声目标的非线性研究开辟了一个新的途径．     

一类位于加周期分岔中的貌似混沌的随机神经放电节律的识别

识别非周期神经放电节律是混沌还是随机一直是一个重要的科学问题. 在神经起步点实验中发现了一类介于周期k和周期k+1(k=1,2)节律之间非周期自发放电节律, 其行为是长串的周期k簇和周期k+1簇的交替. 确定性理论模型Chay模型展示出了周期k和周期k+1节律的共存行为. 噪声在共存区诱发出了与实验结果类似的非周期节律, 说明该类节律是噪声引起的两类簇的跃迁. 非线性预报及其回归映射揭示该节律具有确定性机理; 将两类簇分别转换为0和1得到一个二进制序列, 对该序列进行概率分析获得了两类簇跃迁的随机机理. 这不仅说明该节律是具有确定性结构的随机节律而不是混沌, 还为深入识别现实神经系统的混沌和随机节律提供了典型示例和有效方法.     

物理真随机码发生器随机性分析

对物理随机码发生器的物理参量与其产生的随机码序列的随机性关系进行了分析.根据量子保密通信对随机码序列的随机性的要求，分析了常见的随机码发生器产生的随机码的随机性，给出了利用随机高斯噪音经比较器产生随机码的随机码发生器的随机性公式.     

金属互连电迁移噪声的相关维数研究

针对金属铝互连中噪声信号随电迁移过程变化规律及其所反映的内部失效机理问题，提出将相关维数用于对电迁移噪声时间序列的分析.通过对互连电迁移噪声实验数据的相关维数计算，发现随着电迁移的进行，金属铝互连噪声由随机性成分占主导变为确定性成分占主导，反映出噪声由随机信号转变为混沌动力学信号.应用散射理论解释上述现象，在金属互连电迁移中，空位扩散阶段噪声主要产生机制是空位随机散射；在空位聚集到空洞成核这一过程中，噪声产生机制逐渐从随机散射转变到弹道混沌腔输运机制为主.通过与传统表征参量的对比，证明相关维数可用于预测金 关键词： 电迁移 噪声 相关维数 混沌     

蝙蝠听觉神经系统如何在复杂环境中识别昆虫

生物声纳的高灵敏度和高可靠性一直是仿生设计所追求的目标, 然而至今仍没有一个令人信服的物理模型能很好得解释生物声纳优越性能的原因, 其主要是缺乏对动物听觉系统神经信息编码的认识. 本文从蝙蝠听觉神经系统的生理结构出发, 用圆映射和符号动力学方法讨论了蝙蝠听觉神经系统在复杂环境中处理多普勒信号的一种可能性方案, 并通过计算机仿真证明了其合理性. 针对蝙蝠神经系统的不稳定性, 用符号动力学的方法分析神经系统信息处理的机理具有良好的鲁棒性和高灵敏度. 这种新的信号处理方法的研究, 为生物声纳信号的处理过程的进一步认识提供了一种新的解释.     

混沌信号在光纤传输过程中的非线性演化

对光纤混沌传输理论及混沌信号与光纤传输媒介相互作用的物理机制进行了理论研究. 通过耦合激光混沌系统和光纤传输信道，提出光纤混沌信号传输的非线性演化物理模型. 着重分析光纤自相位调制对激光混沌信号传输与演化的作用. 结果表明：自相位调制不影响混沌信号脉冲的形状，但能产生非线性相移使混沌信号频谱展宽；自相位调制不影响混沌信号脉冲的功率分布和场强分布，但能影响混沌信号脉冲的功率频谱分布，影响混沌信号光场以及慢变场的变化. 提出混沌信号在光纤传输中的非线性演化频率啁啾和形式，数值模拟了混沌信号在光纤传输过程中的相位、频谱、场以及场的慢变部分的相空间等演化形式和特点. 关键词： 混沌 光纤 传输 演化     

Contrast Adaptation Decreases Complexity in Retinal Ganglion Cell Spike Train

The difference in temporal structures of retinal ganglion cell spike trains between spontaneous activity and firing activity after contrast adaptation is investigated. The Lempel-Ziv complexity analysis reveals that the complexity of the neural spike train decreases after contrast adaptation. This implies that the behaviour of the neuron becomes ordered, which may carry relevant information about the external stimulus. Thus, during the neuron activity after contrast adaptation, external information could be encoded in forms of some certain patterns in the temporal structure of spike train that is significantly different, compared to that of the spike train during spontaneous activity, although the firing rates in spontaneous activity and firing activity after contrast adaptation are sometime similar.     

Rhythm Synchronization of Coupled Neurons with Temporal Coding Scheme

Encoding information by firing patterns is one of the basic neural functions, and synchronization is important collective behaviour of a group of coupled neurons. Taking account of two schemes for encoding information （that is, rate coding and temporal coding）, rhythm synchronization of coupled neurons is studied. There are two types of rhythm synchronization of neurons： spike and burst synchronizations. Firstly, it is shown that the spike synchronization is equivalent to the phase synchronization for coupled neurons. Secondly, the similarity function of the slow variables of neurons, which have relevant to the bursting process, is proposed to judge the burst synchronization. It is also found that the burst synchronization can be achieved more easily than the spike synchronization, whatever the firing patterns of the neurons are. Hence the temporal encoding scheme, which is closely related to both the spike and burst synchronizations, is more comprehensive than the rate coding scheme in essence.     

Coding of concurrent vocal signals by the auditory midbrain: effects of stimulus level and depth of modulation

The segregation of concurrent vocal signals is an auditory processing task faced by all vocal species. To segregate concurrent signals, the auditory system must encode the spectral and temporal features of the fused waveforms such that at least one signal can be individually detected. In the plainfin midshipman fish (Porichthys notatus), the overlapping mate calls of neighboring males produce acoustic beats with amplitude and phase modulations at the difference frequencies (dF) between spectral components. Prior studies in midshipman have shown that midbrain neurons provide a combinatorial code of the temporal and spectral characteristics of beats via synchronization of spike bursts to dF and changes in spike rate and interspike intervals with changes in spectral composition. In the present study we examine the effects of changes in signal parameters of beats (overall intensity level and depth of modulation) on the spike train outputs of midbrain neurons. The observed changes in spike train parameters further support the hypothesis that midbrain neurons provide a combinatorial code of the spectral and temporal features of concurrent vocal signals.     

Pitch matches between unresolved complex tones differing by a single interpulse interval

The experiment compared the pitches of complex tones consisting of unresolved harmonics. The fundamental frequency (F0) of the tones was 250 Hz and the harmonics were bandpass filtered between 5500 and 7500 Hz. Two 20-ms complex-tone bursts were presented, separated by a brief gap. The gap was an integer number of periods of the waveform: 0, 4, or 8 ms. The envelope phase of the second tone burst was shifted, such that the interpulse interval (IPI) across the gap was reduced or increased by 0.25 or 0.75 periods (1 or 3 ms). A "no shift" control was also included, where the IPI was held at an integer number of periods. Pitch matches were obtained by varying the F0 of a comparison tone with the same temporal parameters as the standard but without the shift. Relative to the no-shift control, the variations in IPI produced substantial pitch shifts when there was no gap between the bursts, but little effect was seen for gaps of 4 or 8 ms. However, for some conditions with the same IPI in the shifted interval, an increase in the IPI of the comparison interval from 4 to 8 ms (gap increased from 0 to 4 ms) changed the pitch match. The presence of a pitch shift suggests that the pitch mechanism is integrating information across the two tone bursts. It is argued that the results are consistent with a pitch mechanism employing a long integration time for continuous stimuli that is reset in response to temporal discontinuities. For a 250-Hz F0, an 8-ms IPI may be sufficient for resetting. Pitch models based on a spectral analysis of the simulated neural spike train, on an autocorrelation of the spike train, and on the mean rate of pitch pulses, all failed to account for the observed pitch matches.     

Coding and computation with neural spike trains

We study a simple model for the statistics of neural spike trains as they encode a continuously varying signal. The model is motivated with reference to several recent experiments on sensory neurons, and we show how analogies between the relevant probabilistic issues in neural coding and statistical mechanics can be exploited. Results are given for the information capacity of the code, for the optimal structure of code-reading algorithms, and for the time delays which arise in optimal processing of the coded signal. In addition, we show how simple analog computations can be expressed directly in terms of transformations of the spike train. The rules for reading the code and for optimal analog computation depend on the context for behavioral decision making-the relative weights assigned to different types of errors, the relative importance of different signals. We find that there is a conflict between minimizing this context dependence of the code and maximizing its information capacity; a compromise can be achieved by appropriate preprocessing (filtering) of the encoded signal. Experiments on auditory and visual neurons qualitatively confirm the predicted filtering. Similarly, the structure of the optimal multiplier neuron is shown to depend upon the intensity and spectral content of incoming signals, and these predictions compare favorably with experiments on a movement-sensitive cell in the fly visual system.     

Bridging rate coding and temporal spike coding by effect of noise

It is controversial whether temporal spike coding or rate coding is dominant in the information processing of the brain. We show by a two-layered neural network model with noise that, when noise is small, cortical neurons fire synchronously and intervals of synchronous firing robustly encode the signal information, but that the neurons desynchronize with moderately strong noise to encode waveforms of the signal more accurately. Further increase of noise just deteriorates the encoding. A positive role of noise in the brain is suggested in a meaning different from stochastic resonance, coherence resonance, and deterministic chaos.     

Equalization of synaptic efficacy by synchronous neural activity

It is commonly believed that spike timings of a postsynaptic neuron tend to follow those of the presynaptic neuron. Such orthodromic firing may, however, cause a conflict with the functional integrity of complex neuronal networks due to asymmetric temporal Hebbian plasticity. We argue that reversed spike timing in a synapse is a typical phenomenon in the cortex, which has a stabilizing effect on the neuronal network structure. We further demonstrate how the firing causality in a synapse is perturbed by synchronous neural activity and how the equilibrium property of spike-timing dependent plasticity is determined principally by the degree of synchronization. Remarkably, even noise-induced activity and synchrony of neurons can result in equalization of synaptic efficacy.     

Temporal correlations and neural spike train entropy.

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a "brute force" approach.     

Topological analysis of chaos in neural spike train bursts

We show how a topological model which describes the stretching and squeezing mechanisms responsible for creating chaotic behavior can be extracted from the neural spike train data. The mechanism we have identified is the same one ("gateau roule," or jelly-roll) which has previously been identified in the Duffing oscillator [Gilmore and McCallum, Phys. Rev. E 51, 935 (1995)] and in a YAG laser [Boulant et al., Phys. Rev. E 55, 5082 (1997)]. (c) 1999 American Institute of Physics.