Analytical reconstruction of the neuronal input current from spike train data

The time course of the current driving action potential generation at a neuron investigated experimentally is in general not measurable directly. In this paper an indirect method is introduced that allows estimation of this unknown current time course using only spike train data. Assuming the leaky integrator model as valid for the action potential encoding site of the investigated neuron, the unknown input current is obtained by determining (analytically) a current time course that upon injection into the leaky integrator model evokes action potential sequences identical to those observed experimentally. Applications of this current-reconstruction procedure to neuronal output data obtained from a leaky integrator model showed that the procedure allows a good estimation of the underlying input current even if the membrane time constant of the investigated neuron is not known exactly. Additionally, an application of current reconstruction to experimental data obtained from a cat muscle spindle primary afferent subject to repeated -stimuli is demonstrated.     

Nonlinear principal components analysis of neuronal spike train data

Many recent approaches to decoding neural spike trains depend critically on the assumption that for low-pass filtered spike trains, the temporal structure is optimally represented by a small number of linear projections onto the data. We therefore tested this assumption of linearity by comparing a linear factor analysis technique (principal components analysis) with a nonlinear neural network based method. It is first shown that the nonlinear technique can reliably identify a neuronally plausible nonlinearity in synthetic spike trains. However, when applied to the outputs from primary visual cortical neurons, this method shows no evidence for significant temporal nonlinearities. The implications of this are discussed. Received: 29 November 1996 / Accepted in revised form: 1 July 1997     

Sensitivity of different stimulus-timing strategies for the detection of small excitations in noisy spike train data

There are several different strategies to control the timing of a stimulus with respect to the ongoing discharge during the recording of neuronal stimulus-response characteristics. One possible strategy consists of delivering stimuli in such a way that a constant pre-stimulus spike density is reached. Another strategy enforces spike application with a constant stimulus latency after a spontaneous discharge. In this paper the sensitivity of these different strategies for statistical verification of small excitatory response components was investigated. It was found that the difference between observed poststimulus spike distribution and expected spike distribution under the null hypothesis of no stimulus effect was larger using a constant-stimulus-latency (CSL) strategy with an appropriate value for the stimulus latency. Thus, the statistical verification of neuronal response components is clearly facilitated if a CSL strategy is used. This superiority of the CSL strategy is marked, especially for small excitations at neurons discharging slowly with low discharge variability.     

Fourier analysis of spike train data

A formal representation of nerve spike trains in the form of a sum of rectangular functions is proposed. This formal instantaneous frequency function can be Fourier analyzed. The resulting algorithm has the useful properties of spike by spike calculations and an insensitivity to the mean (carrier) spike rate. The technique is also useful for producing a smooth (filtered) reconstruction of a spike train.     

A method of neuronal spike train study based on autoregressive analysis

The method of autoregressive (AR) analysis for neuronal spike trains (NST) is proposed in the paper. The AR model and the Green's function as well as the power spectral density function are used to process and analyse the neuronal interspike interval (ISI) sequences of cat's first somatosensory area of cortex (SI area) under various situations. With these methods the characteristics of the ISI sequence such as the AR order and parameters, memory property, correlativity and periodicity etc. can be extracted.     

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.     

Causal pattern recovery from neural spike train data using the Snap Shot Score

We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.     

An information-geometric framework for statistical inferences in the neural spike train space

Statistical inferences are essentially important in analyzing neural spike trains in computational neuroscience. Current approaches have followed a general inference paradigm where a parametric probability model is often used to characterize the temporal evolution of the underlying stochastic processes. To directly capture the overall variability and distribution in the space of the spike trains, we focus on a data-driven approach where statistics are defined and computed in the function space in which spike trains are viewed as individual points. To this end, we at first develop a parametrized family of metrics that takes into account different warpings in the time domain and generalizes several currently used spike train distances. These new metrics are essentially penalized L ^p norms, involving appropriate functions of spike trains, with penalties associated with time-warping. The notions of means and variances of spike trains are then defined based on the new metrics when p = 2 (corresponding to the “Euclidean distance”). Using some restrictive conditions, we present an efficient recursive algorithm, termed Matching-Minimization algorithm, to compute the sample mean of a set of spike trains with arbitrary numbers of spikes. The proposed metrics as well as the mean spike trains are demonstrated using simulations as well as an experimental recording from the motor cortex. It is found that all these methods achieve desirable performance and the results support the success of this novel framework.     

Effect of stimulus intensity on spike responses of crayfish receptor neurons

The character of spike responses of receptor neurons of n. telsoni ventralis to changes in the direction, velocity, and angle of displacement of the uropod hairs was studied in experiments onAstacus leptodactylus. For all receptor neurons the latent period is inversely proportional and the mean impulse frequency directly proportional to the velocity of hair displacement. Selective sensitivity of different receptor neurons to the direction of motion and also to a change in the angle of displacement of the hair was discovered; certain zones of angular displacements of the hair characterized by a maximal response of the receptor neuron were distinguished.     

Conductance versus current noise in a neuronal model for noisy subthreshold oscillations and related spike generation

Biological systems are notoriously noisy. Noise, therefore, also plays an important role in many models of neural impulse generation. Noise is not only introduced for more realistic simulations but also to account for cooperative effects between noisy and nonlinear dynamics. Often, this is achieved by a simple noise term in the membrane equation (current noise). However, there are ongoing discussions whether such current noise is justified or whether rather conductance noise should be introduced because it is closer to the natural origin of noise. Therefore, we have compared the effects of current and conductance noise in a neuronal model for subthreshold oscillations and action potential generation. We did not see any significant differences in the model behavior with respect to voltage traces, tuning curves of interspike intervals, interval distributions or frequency responses when the noise strength is adjusted. These findings indicate that simple current noise can give reasonable results in neuronal simulations with regard to physiological relevant noise effects.     

成年小鼠前脑NMDA受体参与神经元的动作电位发放

谷氨酸是中枢神经系统主要的快速兴奋性递质。AMPA受体和海人藻酸受体主要参与突触传递,而NMDA受体主要参与突触可塑性。基因操作的方法增强NMDA受体的功能,可以增强动物在正常生理状态下的学习能力,及在组织损伤情况下的反应敏感性。NMDA受体参与生理功能的主要机制是长时程增强（long—term potentiation,LTP）。我们的研究表明,NMDA受体不仅参与刺激前扣带皮层的第五层细胞或刺激白质诱导的突触反应,而且参与在胞体施加去极化跃阶电流诱导的动作电位的发放。钙一钙调蛋白敏感的腺苷酸环化酶1（adenylyl cyclase 1,AC1）和cAMP信号通路可能介导了这些反应。由于扣带皮层神经元在伤害性刺激和痛中发挥重要作用,我们的结果为前脑NMDA受体参与突触传递和动作电位发放,以及与前脑相关的行为,如感受伤害性刺激和痛,提供了一个新的机制。     

A comparative statistical study of hippocampal neuronal spontaneous spike activity in situ and in vitro

The statistical characteristics of the spontaneous spike activity of rat hippocampal neurons in fields CA_1?2 were compared in situ and in tissue culture. Statistical analyses have shown strong similarities in estimators of basic numerical characteristics of interspike interval (ISI) distributions. These similarities may serve as evidence of maintenance of normal functional properties and an “organotypic arrangement” of neurons in tissue culture, and they are also indicative of an intrahippocampal origin of the spontaneous impulse activity in the hippocampus. On the other hand, some differences are noted in the tests of firing patterns. Interpretation of these results leads to some assumptions about mechanisms of the phenomenon under study.     

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.     

Pooling of impulse sequences,with emphasis on applications to neuronal spike data

Summary A mathematical model is presented that is supposed to describe those types of neuronal discharges which show a preponderance of short intervals, as well as one or more preferred intervals of a longer duration. It is assumed that via two channels impulses impinge upon a nerve cell and that each impulse gives rise to a response. The intervals between impulses in one channel are distributed according to an exponential, or an exponential-like, function; those in the other channel are distributed according to a monomodal, or a multimodal, function.The interval distributions and the expectation density (auto-correlation) functions of the model are in particular compared with data on thalamic neuron discharge patterns reported in the literature.The properties of superimposed time series of events would seem to be of a wider interest, stretching beyond the field of theoretical neurophysiology. It is indicated how the theory is of use in the detection of hidden rhythms in records which are composed of a mixture of different signals.     

Neuronal spike trains and stochastic point processes. I. The single spike train

In a growing class of neurophysiological experiments, the train of impulses (“spikes”) produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains.     

Alias-free sampling of neuronal spike trains

Summary Spectral analysis provides powerful techniques for describing the lower order moments of a stochastic process and interactions between two or more stochastic processes. A major problem in the application of spectral analysis to neuronal spike trains is how to obtain equispaced samples of the spike trains which will give unbiased and alias-free spectral estimates. Various sampling methods, which treat the spike train as a continuous signal, a point process and as a series of Dirac delta-functions, are reviewed and their limitations discussed. A new sampling technique, which gives unbiased and alias-free estimates, is described. This technique treats the spike train as a series of delta functions and generates samples by digital filtering. Implementation of this technique on a small computer is simple and virtually on-line.