Summation of Spatiotemporal Input Patterns in Leaky Integrate-and-Fire Neurons: Application to Neurons in the Cochlear Nucleus Receiving Converging Auditory Nerve Fiber Input

The response of leaky integrate-and-fire neurons is analyzed for periodic inputs whose phases vary with their spatial location. The model gives the relationship between the spatial summation distance and the degree of phase locking of the output spikes (i.e., locking to the periodic stochastic inputs, measured by the synchronization index). The synaptic inputs are modeled as an inhomogeneous Poisson process, and the analysis is carried out in the Gaussian approximation. The model has been applied to globular bushy cells of the cochlear nucleus, which receive converging inputs from auditory nerve fibers that originate at neighboring sites in the cochlea. The model elucidates the roles played by spatial summation and coincidence detection, showing how synchronization decreases with an increase in both frequency and spatial spread of inputs. It also shows under what conditions an enhancement of synchronization of the output relative to the input takes place.     

Analytical and Simulation Results for Stochastic Fitzhugh-Nagumo Neurons and Neural Networks

An analytical approach is presented for determining the response of a neuron or of the activity in a network of connected neurons, represented by systems of nonlinear ordinary stochastic differential equations—the Fitzhugh-Nagumo system with Gaussian white noise current. For a single neuron, five equations hold for the first- and second-order central moments of the voltage and recovery variables. From this system we obtain, under certain assumptions, five differential equations for the means, variances, and covariance of the two components. One may use these quantities to estimate the probability that a neuron is emitting an action potential at any given time. The differential equations are solved by numerical methods. We also perform simulations on the stochastic Fitzugh-Nagumo system and compare the results with those obtained from the differential equations for both sustained and intermittent deterministic current inputs withsuperimposed noise. For intermittent currents, which mimic synaptic input, the agreement between the analytical and simulation results for the moments is excellent. For sustained input, the analytical approximations perform well for small noise as there is excellent agreement for the moments. In addition, the probability that a neuron is spiking as obtained from the empirical distribution of the potential in the simulations gives a result almost identical to that obtained using the analytical approach. However, when there is sustained large-amplitude noise, the analytical method is only accurate for short time intervals. Using the simulation method, we study the distribution of the interspike interval directly from simulated sample paths. We confirm that noise extends the range of input currents over which (nonperiodic) spike trains may exist and investigate the dependence of such firing on the magnitude of the mean input current and the noise amplitude. For networks we find the differential equations for the means, variances, and covariances of the voltage and recovery variables and show how solving them leads to an expression for the probability that a given neuron, or given set of neurons, is firing at time t. Using such expressions one may implement dynamical rules for changing synaptic strengths directly without sampling. The present analytical method applies equally well to temporally nonhomogeneous input currents and is expected to be useful for computational studies of information processing in various nervous system centers.     

Nonlinear Relationships in the Patterns of Neuronal Spiking in Cortical Neurons

The pattern of neuronal spiking of cortical neurons was investigated in an awake nonimmobilized rabbit. Thecharacteristics of the interspike intervals (total numberof intervals, mean interval, mean-square deviation) and of the burst (group) activity (burst number, mean spikefrequency in a burst, mean spike number for a burst, meanburst duration) were considered. Nonlinear relationshipbetween the values of mean interspike intervals and thenumber of spike bursts was found. A number of functionswere applied to describe the observed phenomena. On thebasis of regression analysis two populations of corticalneurons with distinct neuronal spiking patterns wereidentified. Bursts occur at a higher rate in one populationthan the other, although both populations exhibit burstsand are otherwise indistinguishable.     

听觉中枢神经元对声信号的识别和处理

人类听觉的基本特性和机制与其他哺乳动物相似,因此,利用动物所作的听觉研究和获得的结果,有助于认识人类自身的听觉.围绕听觉中枢神经元对不同模式的声信号的识别和处理,简要综述了这方面的研究.声信号和声模式识别在听觉中枢对声信号的感受和加工中具有重要意义.听神经元作为声模式识别的结构和功能基础,对不同的声刺激模式产生不同反应,甚至是在同一声刺激模式下,改变其中的某个声参数,神经元的反应也会发生相应改变,而其反应的特性和机制均需要更多研究来解答.另外,声信号作为声信息的载体,不同的声信息寓于不同的声参数和声特征之中,研究发现,听觉中枢神经元存在相应的声信息甄别和选择的神经基础,能对动态变化的声频率、幅度和时程等进行反应和编码,并且,在不同种类动物上获得的研究结果极为相似,表明听觉中枢对不同声信号和声刺激模式的识别、分析和加工,具有共同性和普遍性.     

大鼠生后发育过程中听皮层神经元特征频率的可塑性

应用常规电生理学技术,以神经元的特征频率和频率调谐曲线为指标,分别在生后2、3、4、5、6和8周龄SD大鼠上,研究生后发育过程中,听皮层神经元特征频率的可塑性.结果表明,在给予条件刺激频率和神经元特征频率相差1.0kHz范围内,条件刺激都可诱导各年龄组神经元特征频率向频率调谐曲线的低频端、高频端或调谐曲线的两端相应的偏移.特征频率偏移的概率与年龄相关.随着年龄的增长,特征频率偏移的比例下降,而不偏移的比例则上升.随着年龄增长,那些Q10-dB值大和频率调谐曲线对称指数大于零的神经元,特征频率偏移到频率调谐曲线高频端的比例增加更为明显(P<0.01).诱导特征频率完全偏移的时程和特征频率恢复的时程也与动物的年龄相关,随着年龄增长,诱导和恢复时程都明显延长(P<0.05).结果提示,大鼠听皮层神经元特征频率的可塑性与生后年龄相关,为深入研究中枢神经元功能活动可塑性的机制提供了重要实验资料.     

预测性编码在视听觉神经活动中的典型表征

现代神经科学研究指出,大脑是外部世界的“预测器”,它能根据先验知识和当前信息对即将到来的感觉信息进行主动估计,从而完成与外部世界的高效交互。预测性编码是描述预期作用机制的主要理论模型,梳理其在解释视、听觉神经现象方面的研究进展,可为深入理解大脑工作模式提供新的理论基础。本文简述了预测性编码的内容;从常用范式、典型现象、面临争议等方面梳理预期与感觉输入相互作用的典型研究;从有预期无刺激的神经表征、预期相关神经振荡模式两方面简述预期独立于刺激的内源性神经表征;进而回顾了支持预测性编码中分级结构的神经生理证据及重要神经结构。最后,本文从深化理论研究、助力疾病诊疗、启发脑-机接口技术等方面对预测性编码相关研究的发展进行了展望。深入理解预测性编码在视、听觉神经活动中的计算模型及神经表征,有望为揭示大脑感知觉神经活动工作模式开辟新途径。     

CF-FM蝙蝠听中枢神经元处理行为相关声信号的神经机制

蝙蝠具有高度发达的回声定位系统,能够准确地处理和整合不断变化环境中的声学参数,以保持最佳的生理和行为状态。这种行为的神经生理机制已经得到了广泛的研究。本文主要探究了CF-FM蝙蝠听觉中枢处理种属特异性声信号、共变参数、多普勒频移补偿信号及多谐波声信号的神经机制,可有助于了解回声定位蝙蝠处理行为相关声信号的神经策略。同时本文也提出将来可以CF-FM蝙蝠作为模式动物进行更深入的胞内研究。     

The Use of a Symbol Processing Computer Language in Point Estimation of Parameters and in Construction of Confidence Intervals

This paper presents a method to generate automatically computer programs which are necessary for parameter estimation, hypothesis tests and construction of confidence intervals by the maximum likelihood method. The spectral or density function of the random variable is arbitrary, but must be known and given in closed form. The programming language used is the symbol processing language LIBAFORM, whose statements are interpreted by a package of LISP-routines. The application of the method is illustrated by the analysis of a linear model whose residuals follow a logarithmic F-distribution, and the analysis of a dose-response curve.     

Comparison of Coding Capabilities of Type I and Type II Neurons

We consider the dependence of information transfer by neurons on the Type I vs. Type II classification of their dynamics. Our computational study is based on Type I and II implementations of the Morris-Lecar model. It mainly concerns neurons, such as those in the auditory or electrosensory system, which encode band-limited amplitude modulations of a periodic carrier signal, and which fire at random cycles yet preferred phases of this carrier. We first show that the Morris-Lecar model with additive broadband noise ("synaptic noise") can exhibit such firing patterns with either Type I or II dynamics, with or without amplitude modulations of the carrier. We then compare the encoding of band-limited random amplitude modulations for both dynamical types. The comparison relies on a parameter calibration that closely matches firing rates for both models across a range of parameters. In the absence of synaptic noise, Type I performs slightly better than Type II, and its performance is optimal for perithreshold signals. However, Type II performs well over a slightly larger range of inputs, and this range lies mostly in the subthreshold region. Further, Type II performs marginally better than Type I when synaptic noise, which yields more realistic baseline firing patterns, is present in both models. These results are discussed in terms of the tuning and phase locking properties of the models with deterministic and stochastic inputs.     

生态过程模型敏感参数最优取值的时空异质性分析——以BIOME-BGC模型为例

生态过程模型是当前研究陆地生态系统水循环、碳循环有力的工具,但此类模型参数众多,参数的合理取值对模型模拟结果有重要影响.以往研究对模型参数的敏感性以及参数的优化取值有诸多的分析和讨论,但有关参数最优取值的时空异质性关注较少.本文以BIOME-BGC模型为例,在常绿阔叶林、落叶阔叶林、C3草地3种植被类型下,通过构建敏感性判别指数,筛选出模型的敏感参数,并在每种植被类型下选取两个试验站点,使用模拟退火算法结合实测通量数据构建目标函数,获取各站点敏感参数逐月的最优取值,然后构建时间异质性判别指数、空间异质性判别指数和时空异质性判别指数对模型敏感参数最优取值的时空异质性进行定量分析.结果表明:BIOME-BGC模型在3种植被类型下遴选出的敏感参数大部分一致,少数有差异,但参数的敏感性强弱在不同植被类型下的表现不尽相同;BIOME-BGC模型敏感参数的最优取值,大都具有不同程度的时空异质性,但不同植被类型下,敏感参数最优取值的时空异质性表现各异;敏感参数中与植被生理、生态相关的参数,其时空异质性相对较小,而与环境、物候相关的参数,其时空异质性普遍较大;在3种植被类型下,模型敏感参数最优取值的时间异质性与空间异质性表现出显著的线性相关性;依据其最优取值的时空异质性,可对BIOME-BGC模型敏感参数进行类型划分,以便在实践应用中采取不同的参数率定策略.本研究结论有助于加深对生态过程模型参数特性及最优取值的理解,可为实践应用中模型参数的合理取值提供一种思路和参考.