Valuations for spike train prediction

The ultimate product of an electrophysiology experiment is often a decision on which biological hypothesis or model best explains the observed data. We outline a paradigm designed for comparison of different models, which we refer to as spike train prediction. A key ingredient of this paradigm is a prediction quality valuation that estimates how close a predicted conditional intensity function is to an actual observed spike train. Although a valuation based on log likelihood (L) is most natural, it has various complications in this context. We propose that a quadratic valuation (Q) can be used as an alternative to L. Q shares some important theoretical properties with L, including consistency, and the two valuations perform similarly on simulated and experimental data. Moreover, Q is more robust than L, and optimization with Q can dramatically improve computational efficiency. We illustrate the utility of Q for comparing models of peer prediction, where it can be computed directly from cross-correlograms. Although Q does not have a straightforward probabilistic interpretation, Q is essentially given by Euclidean distance.     

Strictly positive-definite spike train kernels for point-process divergences

Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.     

Messages of oscillatory correlograms: a spike train model

Oscillatory correlograms are widely used to study neuronal activity that shows a joint periodic rhythm. In most cases, the statistical analysis of cross-correlation histograms (CCH) features is based on the null model of independent processes, and the resulting conclusions about the underlying processes remain qualitative. Therefore, we propose a spike train model for synchronous oscillatory firing activity that directly links characteristics of the CCH to parameters of the underlying processes. The model focuses particularly on asymmetric central peaks, which differ in slope and width on the two sides. Asymmetric peaks can be associated with phase offsets in the (sub-) millisecond range. These spatiotemporal firing patterns can be highly consistent across units yet invisible in the underlying processes. The proposed model includes a single temporal parameter that accounts for this peak asymmetry. The model provides approaches for the analysis of oscillatory correlograms, taking into account dependencies and nonstationarities in the underlying processes. In particular, the auto- and the cross-correlogram can be investigated in a joint analysis because they depend on the same spike train parameters. Particular temporal interactions such as the degree to which different units synchronize in a common oscillatory rhythm can also be investigated. The analysis is demonstrated by application to a simulated data set.     

基于梯度下降的脉冲神经元精确序列学习算法

基于梯度下降的脉冲神经元有监督学习算法通过计算梯度最小化目标序列和实际输出序列间的误差使得神经元能激发出目标脉冲序列。然而该算法中的误差函数是基于实际输出脉冲序列和相对应的目标输出脉冲序列动态构建而成,导致算法在收敛时可能出现实际输出序列的个数和期望输出个数不相等的情况。针对这一缺陷提出了一种改进的脉冲神经元梯度下降学习算法,算法在学习过程中检测目标序列脉冲个数和实际激发脉冲个数,并引入虚拟实际激发脉冲和期望激发脉冲构建误差函数以分别解决激发个数不足和激发个数多余的问题。实验结果证明该算法能有效地防止学习算法在输出脉冲个数不等的情况下提前结束,使得神经元能够精确地激发出目标脉冲序列。     

The time-rescaling theorem and its application to neural spike train data analysis.

Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.     

A comparison of descriptive models of a single spike train by information-geometric measure

In examining spike trains, different models are used to describe their structure. The different models often seem quite similar, but because they are cast in different formalisms, it is often difficult to compare their predictions. Here we use the information-geometric measure, an orthogonal coordinate representation of point processes, to express different models of stochastic point processes in a common coordinate system. Within such a framework, it becomes straightforward to visualize higher-order correlations of different models and thereby assess the differences between models. We apply the information-geometric measure to compare two similar but not identical models of neuronal spike trains: the inhomogeneous Markov and the mixture of Poisson models. It is shown that they differ in the second- and higher-order interaction terms. In the mixture of Poisson model, the second- and higher-order interactions are of comparable magnitude within each order, whereas in the inhomogeneous Markov model, they have alternating signs over different orders. This provides guidance about what measurements would effectively separate the two models. As newer models are proposed, they also can be compared to these models using information geometry.     

A spike train distance-based method to evaluate the response of mechanoreceptive afferents

Neural Computing and Applications - Spike train distances have gained increasing attention in the neuroscience community and provided an important tool to quantify the similarity between spike...     

带翼水下机器人的非线性拟合分段控制方法

S面控制方法可较好地解决水下机器人的运动控制问题,但由于其参数是固定的,无法达到全局最优．不同航渡速度段,采取不同的控制参数值,可保证水动力不同阶段控制输出的最优;但在速度变化的分界点,控制器输出有跳变,不利于系统的全局稳定性．利用T-S模糊系统逼近非线性连续函数的能力,采用非线性的S面函数作为模糊系统的后件,设计了基于T—S模型的S面控制器．通过T-S模型的引入,避免了控制器输出的跳变,增强了系统稳定性．将该方法应用于带翼水下机器人的深度控制,水池试验和湖中实验均证明了算法的有效性．     

求解网络广告资源优化模型的改进微粒群算法

针对网络广告的特点，提出一个最大化广告效果函数的广告资源分配模型．在Langheinrich线性模型的基础上，加入分散广告显示的二次惩罚项，使所得到的最优解更能发挥网络广告的作用，结合微粒群(PSO)算法和模型的特点，设计出能有效处理约束的改进PSO算法，数值仿真证明了算法的有效性。     

Integrated Risk Analysis for a Commercial Computing Service in Utility Computing

Recent technological advances in Grid computing enable the virtualization and dynamic delivery of computing services on demand to realize utility computing. In utility computing, computing services will always be available to the users whenever the need arises, similar to the availability of real-world utilities, such as electrical power, gas, and water. With this new outsourcing service model, users are able to define their service needs through Service Level Agreements (SLAs) and only have to pay when they use the services. They do not have to invest on or maintain computing infrastructures themselves and are not constrained to specific computing service providers. Thus, a commercial computing service will face two new challenges: (i) what are the objectives or goals it needs to achieve in order to support the utility computing model, and (ii) how to evaluate whether these objectives are achieved or not. To address these two new challenges, this paper first identifies four essential objectives that are required to support the utility computing model: (i) manage wait time for SLA acceptance, (ii) meet SLA requests, (iii) ensure reliability of accepted SLA, and (iv) attain profitability. It then describes two evaluation methods that are simple and intuitive: (i) separate and (ii) integrated risk analysis to analyze the effectiveness of resource management policies in achieving the objectives. Evaluation results based on simulation successfully demonstrate the applicability of separate and integrated risk analysis to assess policies in terms of the objectives. These evaluation results which constitute an a posteriori risk analysis of policies can later be used to generate an a priori risk analysis of policies by identifying possible risks for future utility computing situations.     

Job Scheduling for a Computing System

Jobs of a computing system are scheduled by estimating the resources and schedule lengths in a general conveyor system, forced idling of processors, and branches in two-processor homogeneous and nonhomogeneous systems via dynamic programming with regard for the rank levels of dependent jobs in their priority orgraphs. These estimates are applied to find the structure of a computing system from models with partial constraints and without constraints on resources and jobs.     

Computing a basis for a finite abelian p-group

An algorithm for computing a basis for a finite abelian p-group, given a set of generators, is described. The algorithm is particularly suited for use when the elements of the generating set are produced sequentially     

A new algorithm for optimally determining lot-sizing policies for a deteriorating item in an integrated production-inventory system

In this study, we focus on optimally determining lot-sizing policies for a deteriorating item among all the partners in a supply chain system with a single-vendor and multiple-buyers so as to minimize the average total costs. We revise Yang and Wee's [1] model using the Fourier series to precisely estimate the vendor's inventory holding costs. Also, we transform our revised model into a more concise version by applying anapproximation to the exponential terms in the objective function. In order to solve this problem, we analyze the optimality structure of our revised model and derive several interesting properties. By utilizing our theoretical results, we propose a search algorithm that can efficiently solve the optimal solution. Based on our numerical experiments, we show that the proposed algorithm outperforms the existing solution approach in the literature, especially when the number of buyers is larger in the supply chain system.     

A CAVE-like environment as a tool for full-size train design

Simulations of models, in all different areas, is an expanding, attractive line of work. More and more applications are taking advantage of the improvements in technology and knowledge in this field, thus achieving results that would have been impossible to achieve with a real model, or foreseeing facts that otherwise would have been encountered too late in the production process. The rail industry is one possible beneficiary of this approach. Usually, before commencing the fabrication process of a new train, the construction of a full-size model is mandatory. Instead of building this full-size real model, which leaves little room for later, last-minute modifications, a virtual model can be built in the digital realm, thus offering a new platform for easier interaction with it. In this article, a simulation of a train is presented in order to tackle visual, aesthetic and ergonomic issues. The simulation runs on a PC-based CAVE-like architecture, offering a certain degree of interaction to the user, and combines static and dynamic computer-generated imagery, both with and without stereoscopy for 3D visualisation, as well as augmented virtuality techniques for the integration of the train with its environment.     

Some approaches towards constructing optimally efficient multigrid solvers for the inviscid flow equations

Some important advances have taken place during the last few years in the development of genuinely multidimensional upwind schemes for the compressible Euler equations. In particular, a robust, high-resolution genuinely multidimensional scheme which can be used for any of the flow regimes computations has been constructed. This paper summarizes briefly these developments and outlines the fundamental advantages of this approach