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.     

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。     

Temporal heterodyne shearing speckle pattern interferometry

Shearing speckle pattern interferometry is a full-field speckle interferometric technique used to determine surface displacement derivatives. In this paper, a new measurement system of real-time heterodyne shearography interferometry is presented. This system combined with heterodyne measurement, shearography interferometry and time domain signal processing technology can dynamically detect the out-of-plane displacement gradient. The principles and system arrangement are described. Using the Jones matrix, the mathematical expression of light intensity distribution passing through this system is deduced. A preliminary experiment was performed to demonstrate the performance of this new device, and simulations were conducted using the finite element method. Comparison of results shows that quantitative measurement of the displacement derivative has been achieved.     

Temporal context and conditional associative learning

Background  

We investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other objects, while other objects lacked this task-irrelevant but predictive context.     

Temporal autocorrelation functions of solar speckle pattern

A study of day-time temporal autocorrelation functions of speckles was performed on the solar granulation with the McMath telescope of the Kitt Peak National Observatory. In the space of only a few minutes, rapid variations of time constants are observed. For some observations, a single and high value T of time-constant is obtained: average = 170 ms, standard deviation = 80 ms. The temporal autocorrelation functions are consistent with theoretical curves obtained from a single layer atmospheric turbulence model. For other observations, a super-imposed short time-constant τ, showing little variations around 5.5 ms (standard deviation 1 ms), is also present. It is shown that T depends on the diameter of the telescope D and that the value τ of the short time scale, when it occurs, is correlated with the Fried parameter r₀.     

Theory of spike timing-based neural classifiers

We study the computational capacity of a model neuron, the tempotron, which classifies sequences of spikes by linear-threshold operations. We use statistical mechanics and extreme value theory to derive the capacity of the system in random classification tasks. In contrast with its static analog, the perceptron, the tempotron's solutions space consists of a large number of small clusters of weight vectors. The capacity of the system per synapse is finite in the large size limit and weakly diverges with the stimulus duration relative to the membrane and synaptic time constants.     

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.     

Power and halfwidth of first laser spike

Using approximate solutions of the rate equations, the power and halfwidth of the first spike of a pulsed four-level solid-state laser are derived. Experiments show the suitability of the formulae for laser design.     

Time-varying irregularities in multiple trial spike data

The measurement of neuronal firing rates has been a standard methodology for characterizing properties of neurons. The peri-stimulus time histogram (PSTH) is primarily used for visualizing changes of firing rates in relation to an external stimulus or an event. On the other hand, modulation of other statistics such as distribution and patterns of interspike intervals can be an important index for analysis of neuronal response and may provide insights into the neuronal codes. In particular, it is desirable to visualize the temporal modulation not only of the firing rates but also of the other statistics. In this study, we propose an analysis method for measuring irregularities in multiple trial spike data. The method calculates a local measure by extracting a short segment of data within a predefined time bin and connecting them each other. We compare the different data extraction methods in Poisson and gamma processes and show that our proposed method is effective for estimating the statistics of the irregular spike data.     

Temporal networks

A great variety of systems in nature, society and technology–from the web of sexual contacts to the Internet, from the nervous system to power grids–can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names—temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology—rather, we want to make papers readable across disciplines.     

Temporal speckle

Various methods of spatial optics having been transposed into the temporal domain, it seemed of interest to extend the concept of speckle as well. Basic phenomena are described in terms of time variables and it is shown that experiments involving spectral analysis can be easily carried out leading to practical applications. The study of roughness is given as an illustration.     

Temporal graphs

We introduce the idea of temporal graphs, a representation that encodes temporal data into graphs while fully retaining the temporal information of the original data. This representation lets us explore the dynamic temporal properties of data by using existing graph algorithms (such as shortest-path), with no need for data-driven simulations. We also present a number of metrics that can be used to study and explore temporal graphs. Finally, we use temporal graphs to analyse real-world data and present the results of our analysis.     

Thermal spike analysis of low energy ion tracks

The theoretical track diameter of low energy ions in organic materials is usually estimated through the model of dose deposition by delta rays, with results remarkably lower than the experimental values obtained via a replica method and electron microscopy. The track detector used here is Makrofol-E and the ions studied have specific energies between 1.4 and 100 keV/n. To evaluate the problem from another point of view, thermal effects for track formation, a modified version of the “liquid drop model” for insulators was applied. The electronic as well as nuclear energy deposition by an individual ion are considered and the thermal spike evolution is studied. The model allows for the formation of ion tracks in a range of energies previously considered as “forbidden”. There still exists a discrepancy between the experimental data and the track diameters predicted by the model, and although this difference is smaller than the obtained with previous calculations, it suggests the necessity of further adjustments.     

Estimation of network structures only from spike sequences

A neuron, the fundamental element of neural systems, interacts with other neurons, often producing very complicated behavior. To analyze, model, or predict such complicated behavior, it is important to understand how neurons are connected as well as how they behave. In this paper, we propose two measures, the spike time metric coefficient and the partial spike time metric coefficient, to estimate the network structure, that is, the topological connectivity between neurons. The proposed measures are based on the spike time metric and partialization analysis. To check the validity, we applied the proposed measures to asynchronous spike sequences that are produced by a mathematical neural network model. It was found that the proposed measure has high performance for estimating the network structures even though the structures have a complex topology such as a small-world structure or a scale-free structure.     

Temporal Non-locality

In this article I investigate several possibilities to define the concept of “temporal non-locality” within the standard framework of quantum theory. In particular, I analyze the notions of “temporally non-local states”, “temporally non-local events” and “temporally non-local observables”. The idea of temporally non-local events is already inherent in the standard formalism of quantum mechanics, and Basil Hiley recently defined an operator in order to measure the degree of such a temporal non-locality. The concept of temporally non-local states enters as soon as “clock-representing states” are introduced in the context of special and general relativity. It is discussed in which way temporally non-local measurements may find an interesting application for experiments which test temporal versions of Bell inequalities.     

Proposed method of spike suppression in solid-state lasers

A method is proposed to suppress the spikes in the output of solid-state lasers, using passive negative feedback. A saturable absorber, normally used for Q-switching the laser, is used in a modified laser resonator, so that the resonator losses increase with laser intensity. The rate equations of a laser with such a loss mechanism are solved numerically. Numerical results for a ruby laser are presented.