A recipe for optimizing a time-histogram

Abstract The time-histogram method is a handy tool for capturing the instantaneous rate of spike occurrence. In most of the neurophysiological literature, the bin size that critically determines the goodness of the fit of the time-histogram to the underlying rate has been selected by individual researchers in an unsystematic manner. We propose an objective method for selecting the bin size of a time-histogram from the spike data, so that the time-histogram best approximates the unknown underlying rate. The resolution of the histogram increases, or the optimal bin size decreases, with the number of spike sequences sampled. It is notable that the optimal bin size diverges if only a small number of experimental trials are available from a moderately fluctuating rate process. In this case, any attempt to characterize the underlying spike rate will lead to spurious results. Given a paucity of data, our method can also suggest how many more trials are needed until the set of data can be analyzed with the required resolution

IC immunity modeling process validation using on-chip measurements

International audienceDeveloping integrated circuit (IC) immunity models and simulation flow has become one of the major concerns of ICs suppliers to predict whether a chip will pass susceptibility tests before fabrication and avoid redesign cost. This paper presents an IC immunity modeling process including the standard immunity test applied to a dedicated test chip. An on-chip voltage sensor is used to characterize the radio frequency interference propagation inside the chip and thus validate the immunity modeling process

On-Chip Noise Sensor for Integrated Circuit Susceptibility Investigations

page number: 12International audienceWith the growing concerns about electromagnetic compatibility of integrated circuits, the need for accurate prediction tools and models to reduce risks of non-compliance becomes critical for circuit designers. However, on-chip characterization of noise is still necessary for model validation and design optimization. Although different on-chip measurement solutions have been proposed for emission issue characterization, no on-chip measurement methods have been proposed to address the susceptibility issues. This paper presents an on-chip noise sensor dedicated to the study of circuit susceptibility to electromagnetic interferences. A demonstration of the sensor measurement performances and benefits is proposed through a study of the susceptibility of a digital core to conducted interferences. Sensor measurements ensure a better characterization of actual coupling of interferences within the circuit and a diagnosis of failure origins

Morpho-fluvial analysis of headwater catchments: an example from the Central-Eastern Pyrenees

Geomorphometry of headwater catchments has been poorly reported in the Central-Eastern Pyrenees. This study presents a series of parameters obtained for Central-Eastern Pyrenean headwater catchments. The database consists of 3,005 first-and 655 second-order catchments. These catchments have been digitalised, identified, and attributed a value for each parameter. The parameters investigated are divided into three groups: relative to catchments, relative to streams and morpho-hydrological ratios. Histograms reveal similarities between orders for some parameters such as mean slope or orientation, while stream orders seem to condition metrical parameters (area, perimeter, stream length). Streams have been fragmented to assess different values for slope. Values for slope over a small portion of the stream near the outlet seem to show clearer differences between orders. With regard to morpho-hydrological ratios, catchments show better distinctions between orders for the Melton and Lemniscate ratios than for the form factor or the basin elongation. The power-law relationship between catchment area and stream length recognised for large fluvial systems is shown here to follow a linear trend at small values. An attempt to identify the morpho-structural regionalisation differentiating the Axial Pyrenees from the pre-Pyrenees is made based on the parameters. However, applying the methodology to other environments could improve the context of the current results. Similar studies could also benefit from the development of such databases.Postprint (author's final draft

Inferring distributions from observed mRNA and protein copy counts in genetic circuits

Defining distributions of molecule counts produced in the cell can elucidate stochastic dynamics of the underlying biological circuits. For genetic circuits, only a few distributions of messenger RNA and protein counts were reported in literature, so the task is to decide which of these candidate distributions best fit the observed data. In this paper, we present a statistical method to infer distributions of mRNA and protein counts from observed data. The main advantage of this method is that it does not require any prior assumptions or knowledge about underlying chemical reactions. In particular, a given distribution is fitted to the observed copy counts using a histogram with optimized bin sizes in order to reduce the fitting error. The goodness of fit is evaluated by Kolmogorov-Smirnov and chi-square statistical tests to accept or reject the hypothesis that observed molecule counts were generated from given distribution. The distribution fitting also yields the values of distribution parameters, or they can be estimated using the Bayes theorem. These parameters appear to be themselves random processes. The presented statistical framework for analyzing the observed mRNA and protein copy counts is illustrated for a simulated model of lac genetic circuit in Escherichia coli. For reaction rates assumed in the model, the results in literature predict that mRNA and protein counts at steady-state are gamma distributed. Our analysis shows that both mRNA and protein in the lac circuit model can be considered gamma distributed in at least 70% of times from the initial state until steady-state. The shape and scale parameters of observed gamma distributions are also gamma distributed, giving rise to double stochastic processes. More importantly, as shown previously, the distribution parameters are functions of transcription and translation rates, so presented statistical framework can be used to estimate or optimize reaction rates in biochemical systems

Estimating Gene Interactions Using Information Theoretic Functionals

With an abundance of data resulting from high-throughput technologies, like DNA microarrays, a race has been on the last few years, to determine the structures and functions of genes and their products, the proteins. Inference of gene interactions, lies in the core of these efforts. In all this activity, three important research issues have emerged. First, in much of the current literature on gene regulatory networks, dependencies among variables in our case genes - are assumed to be linear in nature, when in fact, in real-life scenarios this is seldom the case. This disagreement leads to systematic deviation and biased evaluation. Secondly, although the problem of undersampling, features in every piece of work as one of the major causes for poor results, in practice it is overlooked and rarely addressed explicitly. Finally, inference of network structures, although based on rigid mathematical foundations and computational optimizations, often displays poor fitness values and biologically unrealistic link structures, due - to a large extend - to the discovery of pairwise only interactions. In our search for robust, nonlinear measures of dependency, we advocate that mutual information and related information theoretic functionals (conditional mutual information, total correlation) are possibly the most suitable candidates to capture both linear and nonlinear interactions between variables, and resolve higher order dependencies. To address these issues, we researched and implemented under a common framework, a selection nonparametric estimators of mutual information for continuous variables. The focus of their assessment was, their robustness to the limited sample sizes and their expansibility to higher dimensions - important for the detection of more complex interaction structures. Two different assessment scenaria were performed, one with simulated data and one with bootstrapping the estimators in state-of-the-art network inference algorithms and monitor their predictive power and sensitivity. The tests revealed that, in small sample size regimes, there is a significant difference in the performance of different estimators, and naive methods such as uniform binning, gave consistently poor results compared with more sophisticated methods. Finally, a custom, modular mechanism is proposed, for the inference of gene interactions, targeting the identi cation of some of the most common substructures in genetic networks, that we believe will help improve accuracy and predictability scores

Assessing debris-flow hazard focusing on statistical morpho-fluvial susceptibility models and magnitude-frequency relationships. Application to the central-eastern Pyrenees

Occurrence of debris flows has received little attention in the Pyrenees, probably due to the small risk faced by most of the debris-flow prone sites in this mountain range. Nevertheless, the event of Biescas, which occurred in august 1996 and causing 87 casualties, demonstrates the existence of high-risk spots in the region and justifies the elaboration of the debris-flow hazard assessment presented in this thesis. Five debris flows, which occurred in 2008, are selected; and site-specific descriptions and analysis, regarding geology and morphology, were performed. The results are compared with worldwide data and some conclusions on hazard assessment are presented. The preliminary analysis of some major Eastern Pyrenean debris flows represents the background for this thesis. The necessity of possessing an inventory of past occurrences is of crucial importance when assessing debris-flow hazard. Criteria of reconnaissance were thought to be visible from aerial viewing. 691 tracks through which debris flows are thought to have travelled have been revealed. Based on debris-flow inventories and using a geographical information system, the debris-flow hazard assessment presented in this thesis takes into account fluvio-morphologic parameters, gathered for every 1st-order catchment as well as every 2nd-order catchment. Mountainous headwaters are a common subject in geomorphological studies. Often investigated at local scale, the geomorphological context in which headwaters evolve has been poorly reported in the Central-Eastern Pyrenees or worldwide. A series of parameters obtained for Central-Eastern Pyrenean headwaters catchments consisting of 3005 1st- and 655 2nd-order catchments are presented. Acquired from a digital elevation model, these catchments have been digitalised, identified and attributed a value for each parameter. Previously reported parameters¿ ranges agree with those presented in this study. For the first time, the ranges of values give details about the Central-Eastern Pyrenees headwater catchments. Data mining techniques are used on the morphometric parameters, to calculate and test three different models. The first model is a logistic regression. The other two are classification trees, which are rather novel susceptibility models associated with debris flows. Results related to the training dataset show that the optimized model¿s performance lies within existing reported range although closer to the lowest end (near 70%). When the models are applied to the test set, the logistic regression seems to offer the best prediction, as training and test set results are very similar in terms of performance. Trees are better at extracting laws from a training set, but validation through a test set gives poorer results for a prediction at regional scale. The determination of magnitude of a historic event can be done by distinguishing its deposits. However this is not a trivial task in debris fans that accumulate deposits, corresponding to consecutive debris flows, especially if only a conventional geomorphological analysis is carried out. The event deposits can be mapped and, subsequently, trees damaged by the flows sampled for dating events. A magnitude-frequency relationship was prepared for El Rebaixader site, at local scale, and is compared to that of the Tordó creek. Moreover, a debris-flow inventory was created in the "Aigüestortes i Estany de Sant Maurici" National Park in the Central Pyrenees, Spain, and this regional magnitude-frequency relationship is compared to that of Rebaixader. Both curves include a strong rollover effect at about 2000 m2, and events larger than this magnitude can be represented by a power law, with an exponent between -1.5 and -1.9. This thesis is a first step toward the assessment of debris-flow hazard in the Central-Eastern Pyrenees. Although a lot of information is provided, more work is still to be done, in order to fully capture debris-flow importance in landscape evolution

Spread spectrum signal characteristic estimation using exponential averaging and an ad-hoc chip rate estimator

This dissertation investigates two methods of spread spectrum (SS) signal characteristic estimation for the two principle types of SS systems, frequency-hopped (FH) and direct sequence SS. The exponential averaging detector is used to detect and estimate the hopped frequencies for a SS-FH signal in the presence of interference signals as well as additive-white-Gaussian-noise (AWGN). The detection method provides an estimate of the AWGN plus interference spectrum using exponential averaging and then generates an estimate of the desired signal spectrum by combining the estimated AWGN plus interference spectrum with the composite (desired signal plus interference plus AWGN) spectrum. Finally, this dissertation evaluates the detector's performance as a function of the exponential coeeficient, the combining method, the probability of false alarm, signal-to-AWGN ratio, and signal-to-interference ratio. The second method of SS signal characteristic estimation uses a signal ad-hoc chip rate estimator (ACRE). The ACRE is used to estimate the chip rate of a half-sine pulse shaped SS direct-sequence signal. The Acre is explained in relation to its similarities and contrasts to the chip rate detector. The components and performance of the ACRE are presented for standard-ACRE. ACRE with additional filtering, and ACRE with incrementing. The additional filtering results in a reduced chip rate search range but yields improved estimation performance and incrementing has the potential for parallel processing, resulting in dramatically decreased computational time, without loss of performance.http://archive.org/details/spreadspectrumsi1094510263Approved for public release; distribution is unlimited

Multivariate Multiscale Analysis of Neural Spike Trains

This dissertation introduces new methodologies for the analysis of neural spike trains. Biological properties of the nervous system, and how they are reflected in neural data, can motivate specific analytic tools. Some of these biological aspects motivate multiscale frameworks, which allow for simultaneous modelling of the local and global behaviour of neurons. Chapter 1 provides the preliminary background on the biology of the nervous system and details the concept of information and randomness in the analysis of the neural spike trains. It also provides the reader with a thorough literature review on the current statistical models in the analysis of neural spike trains. The material presented in the next six chapters (2-7) have been the focus of three papers, which have either already been published or are being prepared for publication. It is demonstrated in Chapters 2 and 3 that the multiscale complexity penalized likelihood method, introduced in Kolaczyk and Nowak (2004), is a powerful model in the simultaneous modelling of spike trains with biological properties from different time scales. To detect the periodic spiking activities of neurons, two periodic models from the literature, Bickel et al. (2007, 2008); Shao and Li (2011), were combined and modified in a multiscale penalized likelihood model. The contributions of these chapters are (1) employinh a powerful visualization tool, inter-spike interval (ISI) plot, (2) combining the multiscale method of Kolaczyk and Nowak (2004) with the periodic models ofBickel et al. (2007, 2008) and Shao and Li (2011), to introduce the so-called additive and multiplicative models for the intensity function of neural spike trains and introducing a cross-validation scheme to estimate their tuning parameters, (3) providing the numerical bootstrap confidence bands for the multiscale estimate of the intensity function, and (4) studying the effect of time-scale on the statistical properties of spike counts. Motivated by neural integration phenomena, as well as the adjustments for the neural refractory period, Chapters 4 and 5 study the Skellam process and introduce the Skellam Process with Resetting (SPR). Introducing SPR and its application in the analysis of neural spike trains is one of the major contributions of this dissertation. This stochastic process is biologically plausible, and unlike the Poisson process, it does not suffer from limited dependency structure. It also has multivariate generalizations for the simultaneous analysis of multiple spike trains. A computationally efficient recursive algorithm for the estimation of the parameters of SPR is introduced in Chapter 5. Except for the literature review at the beginning of Chapter 4, the rest of the material within these two chapters is original. The specific contributions of Chapters 4 and 5 are (1) introducing the Skellam Process with Resetting as a statistical tool to analyze neural spike trains and studying its properties, including all theorems and lemmas provided in Chapter 4, (2) the two fairly standard definitions of the Skellam process (homogeneous and inhomogeneous) and the proof of their equivalency, (3) deriving the likelihood function based on the observable data (spike trains) and developing a computationally efficient recursive algorithm for parameter estimation, and (4) studying the effect of time scales on the SPR model. The challenging problem of multivariate analysis of the neural spike trains is addressed in Chapter 6. As far as we know, the multivariate models which are available in the literature suffer from limited dependency structures. In particular, modelling negative correlation among spike trains is a challenging problem. To address this issue, the multivariate Skellam distribution, as well as the multivariate Skellam process, which both have flexible dependency structures, are developed. Chapter 5 also introduces a multivariate version of Skellam Process with Resetting (MSPR), and a so-called profile-moment likelihood estimation of its parameters. This chapter generalizes the results of Chapter 4 and 5, and therefore, except for the brief literature review provided at the beginning of the chapter, the remainder of the material is original work. In particular, the contributions of this chapter are (1) introducing multivariate Skellam distribution, (2) introducing two definitions of the Multivariate Skellam process in both homogeneous and inhomogeneous cases and proving their equivalence, (3) introducing Multivariate Skellam Process with Resetting (MSPR) to simultaneously model spike trains from an ensemble of neurons, and (4) utilizing the so-called profile-moment likelihood method to compute estimates of the parameters of MSPR. The discussion of the developed methodologies as well as the ``next steps'' are outlined in Chapter 7

Etude de l'immunité des circuits intégrés face aux agressions électromagnétiques (proposition d'une méthode de prédiction des couplages des perturbations en mode conduit)

Avec les progrès technologiques réalisés au cours de ces dernières décennies, la complexité et les vitesses de fonctionnement des circuits intégrés ont beaucoup été augmentées. Bien que ces évolutions aient permis de diminuer les dimensions et les tensions d alimentations des circuits, la compatibilité électromagnétique (CEM) des composants a fortement été dégradée. Identifiée comme étant un verrou technologique, la CEM est aujourd hui l une des principales causes de re-design des circuits car les problématiques liées aux mécanismes de génération et de couplage du bruit ne sont pas suffisamment étudiées lors de leur conception.Ce manuscrit présente donc une méthodologie visant à étudier la propagation du bruit à travers les circuits intégrés par mesures et par simulations. Afin d améliorer nos connaissances sur la propagation d interférences électromagnétiques (IEM) et les mécanismes de couplage à travers les circuits, nous avons conçu un véhicule de test développé dans la technologie SMOS8MV® 0,25 m de Freescale Semiconductor. Dans ce circuit, plusieurs fonctions élémentaires telles qu un bus d E/S et des blocs numériques ont été implémentées. Des capteurs de tensions asynchrones ont également été intégrés sur différentes alimentations de la puce pour analyser la propagation des perturbations injectées sur les broches du composant (injection DPI) et sur les conducteurs permettant d alimenter ce dernier (injection BCI). En outre, nous proposons différents outils pour faciliter la modélisation et les simulations d immunité des circuits intégrés (extraction des modèles de PCB, approches de modélisation des systèmes d injection, méthode innovante permettant de prédire et de corréler les niveaux de tension/ de puissance injectés lors de mesures d immunité conduite, flot de modélisation). Chaque outil et méthode de modélisation proposés sont évalués sur différents cas test. Enfin, pour évaluer notre démarche de modélisation, nous l appliquons sur un bloc numérique de notre véhicule de test et comparons les résultats de simulations aux différentes mesures internes et externes réalisées sur le circuitWith technological advances in recent decades, the complexity and operating speeds of integrated circuits have greatly increased. While these developments have reduced dimensions and supply voltages of circuits, electromagnetic compatibility (EMC) of components has been highly degraded. Identified as a technological lock, EMC is now one of the main causes of circuits re-designs because issues related to generating and coupling noise mechanisms are not sufficiently studied during their design. This manuscript introduces a methodology to study propagation of electromagnetic disturbances through integrated circuits by measurements and simulations. To improve our knowledge about propagation of electromagnetic interferences (EMI) and coupling mechanisms through integrated circuits, we designed a test vehicle developed in the SMOS8MV® 0.25 m technology from Freescale Semiconductor. In this circuit, several basic functions such as I/O bus and digital blocks have been implemented. Asynchronous on-chip voltage sensors have also been integrated on different supplies of the chip to analyze propagation of disturbances injected on supply pins and wires of the component (DPI and BCI injection). In addition, we propose various tools to facilitate modeling and simulations of Integrated Circuit s immunity (PCB model extraction, injection systems modeling approaches, innovative method to predict and correlate levels of voltage / power injected during conducted immunity measurements, modeling flow). Each tool and modeling method proposed is evaluated on different test cases. To assess our modeling approach, we finally apply it on a digital block of our test vehicle and compare simulation results to various internal and external measurements performed on the circuitTOULOUSE-INSA-Bib. electronique (315559905) / SudocSudocFranceF