Optimization of Piezoelectric Electrical Generators Powered by Random Vibrations

This paper compares the performances of a vibrationpowered electrical generators using PZT piezoelectric ceramic associated to two different power conditioning circuits. A new approach of the piezoelectric power conversion based on a nonlinear voltage processing is presented and implemented with a particular power conditioning circuit topology. Theoretical predictions and experimental results show that the nonlinear processing technique may increase the power harvested by a factor up to 4 compared to the Standard optimization technique. Properties of this new technique are analyzed in particular in the case of broadband, random vibrations, and compared to those of the Standard interface.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

Experimentally verified parameter sets for modelling heterogeneous neocortical pyramidal-cell populations

Models of neocortical networks are increasingly including the diversity of excitatory and inhibitory neuronal classes. Significant variability in cellular properties are also seen within a nominal neuronal class and this heterogeneity can be expected to influence the population response and information processing in networks. Recent studies have examined the population and network effects of variability in a particular neuronal parameter with some plausibly chosen distribution. However, the empirical variability and covariance seen across multiple parameters are rarely included, partly due to the lack of data on parameter correlations in forms convenient for model construction. To addess this we quantify the heterogeneity within and between the neocortical pyramidal-cell classes in layers 2/3, 4, and the slender-tufted and thick-tufted pyramidal cells of layer 5 using a combination of intracellular recordings, single-neuron modelling and statistical analyses. From the response to both square-pulse and naturalistic fluctuating stimuli, we examined the class-dependent variance and covariance of electrophysiological parameters and identify the role of the h current in generating parameter correlations. A byproduct of the dynamic I-V method we employed is the straightforward extraction of reduced neuron models from experiment. Empirically these models took the refractory exponential integrate-and-fire form and provide an accurate fit to the perisomatic voltage responses of the diverse pyramidal-cell populations when the class-dependent statistics of the model parameters were respected. By quantifying the parameter statistics we obtained an algorithm which generates populations of model neurons, for each of the four pyramidal-cell classes, that adhere to experimentally observed marginal distributions and parameter correlations. As well as providing this tool, which we hope will be of use for exploring the effects of heterogeneity in neocortical networks, we also provide the code for the dynamic I-V method and make the full electrophysiological data set available

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons

Systems comparison and regression trees

A method for non parametric modelling of dynamical systems was presented in a previous paper . This work intends to propose a new approach for addressing the problem of dynamical systems comparison and detection of abrupt changes . The algorithm that is presented here, relies upon both d-dimensionnal histogram and regression tree estimation, and the use of f-divergences . Illustrations on different non linear systems are provided .Nous proposons ici une application de la méthode de modélisation non linéaire non paramétrique de systèmes dynamiques, présentée dans un précédent article. L'approche proposée dans le cadre de ce travail repose sur une partition récursive de l'espace d'état du système, conduisant à un arbre de régression. Ce modèle fournit une estimation de l'histogramme d-dimensionnel de l'espace des états du système : nous montrons comment l'utilisation de distances ou de divergences entre lois de probabilité permet alors de quantifier les différences dynamiques entre systèmes. Cette approche est illustrée sur deux exemples : la détection de changements de modèles autorégressifs dans une série temporelle et la détection de la présence éventuelle d'un soliton de type « breather » susceptible d'apparaître dans le comportement d'une chaîne d'oscillateurs couplés soumis à un potentiel extérieur

Regression trees for non parametric modeling and time series prediction

We present a non-parametric approach to nonlinear modeling and prediction based on adaptive partitioning of the reconstructed phase space associated with the process . The partitioning method is implemented with a recursive tree-structured algorithm which successively refines the partition by binary splitting where the splitting threshold is determined by a penalized maximum entropy criterion. An analysis of the statistical behavior of the splitting rule suggests a criterion for determining the depth of the tree . The effectiveness of this method is illustrated through comparisons with classical approaches for nonlinear system analysis on the basis of reconstruction error and computational complexity . An important relation between our tree-structured model for the process and generalized non-linear thresholded AR model (ART) is established . We illustrate our method for cases where classical linear prediction is known to be rather ineffective : chaotic signals (measured at the output of a Chua-type electronic circuit), and second order ART signal .Nous présentons une approche non linéaire non paramétrique pour la modélisation et la prédiction de signaux, basée sur une méthode de partition récursive de l'espace des phases reconstruit, associé au système sur lequel le signal est prélevé. La partition de l'espace des phases est obtenue par un algorithme récursif de partition binaire. Les seuils de partition sont déterminés à l'aide d'un critère de maximum d'entropie. Une courte analyse statistique du comportement de ces seuils permet de définir un critère simple d'arrêt de la partition récursive. L'intérêt de cette méthode est illustré par la comparaison avec des méthodes classiques dans le cadre de l'analyse de systèmes non linéaires, ainsi que du point de vue du coût de calcul. Nous présentons un lien important entre cette méthode reposant sur une partition hiérarchique (en arbre) et les modèles non linéaires auto-régressifs à seuils (ART). Dans ce contexte, la méthode présentée est appliquée dans des cas pour lesquels les méthodes linéaires échouent en général : les signaux de chaos (séries expérimentales mesurées sur des circuits électroniques de type Chua), ainsi que sur des séries numériques ART d'ordre deux

\u3ci\u3ePseudomonas syringae\u3c/i\u3e Hrp type III secretion system and effector proteins

Pseudomonas syringae is a member of an important group of Gram-negative bacterial pathogens of plants and animals that depend on a type III secretion system to inject virulence effector proteins into host cells. In P. syringae, hrpyhrc genes encode the Hrp (type III secretion) system, and avirulence (avr) and Hrpdependent outer protein (hop) genes encode effector proteins. The hrpyhrc genes of P. syringae pv syringae 61, P. syringae pv syringae B728a, and P. syringae pv tomato DC3000 are flanked by an exchangeable effector locus and a conserved effector locus in a tripartite mosaic Hrp pathogenicity island (Pai) that is linked to a tRNALeu gene found also in Pseudomonas aeruginosa but without linkage to Hrp system genes. Cosmid pHIR11 carries a portion of the strain 61 Hrp pathogenicity island that is sufficient to direct Escherichia coli and Pseudomonas fluorescens to inject HopPsyA into tobacco cells, thereby eliciting a hypersensitive response normally triggered only by plant pathogens. Large deletions in strain DC3000 revealed that the conserved effector locus is essential for pathogenicity but the exchangeable effector locus has only a minor role in growth in tomato. P. syringae secretes HopPsyA and AvrPto in culture in a Hrp-dependent manner at pH and temperature conditions associated with pathogenesis. AvrPto is also secreted by Yersinia enterocolitica. The secretion of AvrPto depends on the first 15 codons, which are also sufficient to direct the secretion of an Npt reporter from Y. enterocolitica, indicating that a universal targeting signal is recognized by the type III secretion systems of both plant and animal pathogens

Characterization of Fabric-to-Fabric Friction: Application to Medical Compression Bandages

Fabric-to-fabric friction is involved in the action mechanism of medical compression devices such as compression bandages or lumbar belts. To better understand the action of such devices, it is essential to characterize, in their use conditions (mainly pressure and stretch), the frictional properties of the fabrics they are composed of. A characterization method of fabric-to-fabric friction was developed. This method was based on the customization of the fourth instrument of the Kawabata Evaluation System, initially designed for fabric roughness and friction characterization. A friction contactor was developed so that the stretch of the fabric and the applied load can vary to replicate the use conditions. This methodology was implemented to measure the friction coefficient of several medical compression bandages. In the ranges of pressure and bandage stretch investigated in the study, bandage-to-bandage friction coefficient showed very little variation. This simple and reliable method, which was tested for commercially available medical compression bandages, could be used for other medical compression fabrics

From Spiking Neuron Models to Linear-Nonlinear Models

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates

Representation of Dynamical Stimuli in Populations of Threshold Neurons

Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework