A dynamic motion simulator for future European docking systems

Europe's first confrontation with docking in space will require extensive testing to verify design and performance and to qualify hardware. For this purpose, a Docking Dynamics Test Facility (DDTF) was developed. It allows reproduction on the ground of the same impact loads and relative motion dynamics which would occur in space during docking. It uses a 9 degree of freedom, servo-motion system, controlled by a real time computer, which simulates the docking spacecraft in a zero-g environment. The test technique involves and active loop based on six axis force and torque detection, a mathematical simulation of individual spacecraft dynamics, and a 9 degree of freedom servomotion of which 3 DOFs allow extension of the kinematic range to 5 m. The configuration was checked out by closed loop tests involving spacecraft control models and real sensor hardware. The test facility at present has an extensive configuration that allows evaluation of both proximity control and docking systems. It provides a versatile tool to verify system design, hardware items and performance capabilities in the ongoing HERMES and COLUMBUS programs. The test system is described and its capabilities are summarized

Neural Field Model of VSD Optical Imaging Signals

In this report we propose a solution to the direct problem of VSD optical imaging based on a neural field model of a cortical area and reproduce optical signals observed in various mammals cortices. We first present a biophysical approach to neural fields and show that these easily integrate the biological knowledge on cortical structure, especially horizontal and vertical connectivity patterns. After having introduced the reader to VSD optical imaging, we propose a biophysical formula expressing the optical imaging signal in terms of the activity of the field. Then, we simulate optical signals that have been observed by experimentalists. We have chosen two experimental sets: the line-motion illusion in the visual cortex of mammals (jancke, chavane, et al. 2004} and the spread of activity in the rat barrel cortex (petersen, grinvald, et al. 2003). We begin with a structural description of both areas, with a focus on horizontal connectivity. Finally we simulate the corresponding neural field equations and extract the optical signal using the direct problem formula developed in the preceding sections. We have been able to reproduce the main experimental results with these models

Analysis of Jansen's model of a single cortical column

In this report we present a mathematical analysis of a simple model of a cortical column. We first recall some known biological facts about cortical columns. We then present a mathematical model of such a column, developed by a number of people including Lopes Da Silva, Jansen, Rit. Finally we analyze some aspects of its behaviour in the framework of the theory of dynamical systems using bifurcation theory and the software package XPP-Aut developed by B. Ermentrout. This mathematical approach leads us to a compact representation of the model that allows to finally discuss its adequacy with biology

Persistent neural states: stationary localized activity patterns in nonlinear continuous nn-population, qq-dimensional neural networks

Neural continuum networks are an important aspect of the modeling of macroscopic parts of the cortex. Two classes of such networks are considered: voltage- and activity-based. In both cases our networks contain an arbitrary number, $n$, of interacting neuron populations. Spatial non-symmetric connectivity functions represent cortico-cortical, local, connections, external inputs represent non-local connections. Sigmoidal nonlinearities model the relationship between (average) membrane potential and activity. Departing from most of the previous work in this area we do not assume the nonlinearity to be singular, i.e., represented by the discontinuous Heaviside function. Another important difference with previous work is our relaxing of the assumption that the domain of definition where we study these networks is infinite, i.e. equal to $\R$ or $\R^2$. We explicitely consider the biologically more relevant case of a bounded subset $\Omega$ of $\R^q,\,q=1,\,2,\,3$, a better model of a piece of cortex. The time behaviour of these networks is described by systems of integro-differential equations. Using methods of functional analysis, we study the existence and uniqueness of a stationary, i.e., time-independent, solution of these equations in the case of a stationary input. These solutions can be seen as ``persistent'', they are also sometimes called ``bumps''. We show that under very mild assumptions on the connectivity functions and because we do not use the Heaviside function for the nonlinearities, such solutions always exist. We also give sufficient conditions on the connectivity functions for the solution to be absolutely stable, that is to say independent of the initial state of the network. We then study the sensitivity of the solution(s) to variations of such parameters as the connectivity functions, the sigmoids, the external inputs, and, last but not least, the shape of the domain of existence $\Omega$ of the neural continuum networks. These theoretical results are illustrated and corroborated by a large number of numerical experiments in most of the cases $2\leq n \leq 3,\,2\leq q \leq 3$

Bumps in simple two-dimensional neural field models

Neural field models first appeared in the 50's, but the theory really took off in the 70's with the works of Wilson and Cowan {wilson-cowan:72,wilson-cowan:73} and Amari {amari:75,amari:77}. Neural fields are continuous networks of interacting neural masses, describing the dynamics of the cortical tissue at the population level. In this report, we study homogeneous stationary solutions (i.e independent of the spatial variable) and bump stationary solutions (i.e. localized areas of high activity) in two kinds of infinite two-dimensional neural field models composed of two neuronal layers (excitatory and inhibitory neurons). We particularly focus on bump patterns, which have been observed in the prefrontal cortex and are involved in working memory tasks {goldman-rakic:95}. We first show how to derive neural field equations from the spatialization of mesoscopic cortical column models. Then, we introduce classical techniques borrowed from Coombes {coombes:05} and Folias and Bressloff {folias-bressloff:04} to express bump solutions in a closed form and make their stability analysis. Finally we instantiate these techniques to construct stable two-dimensional bump solutions

Stability and Synchronization in Neural Fields

Neural fields are an interesting option for modelling macroscopic parts of the cortex involving several populations of neurons, like cortical areas. Two classes of neural field equations are considered: voltage and activity based. The spatio-temporal behaviour of these fields is described by nonlinear integro-differential equations. The integral term, computed over a compact subset of $\mathbb{R}^q,\,q=1,2,3$, involves space and time varying, possibly non-symmetric, intra-cortical connectivity kernels. Contributions from white matter afferents are represented as external input. Sigmoidal nonlinearities arise from the relation between average membrane potentials and instantaneous firing rates. Using methods of functional analysis, we characterize the existence and uniqueness of a solution of these equations for general, homogeneous (i.e. independent of the spatial variable), and locally homogeneous inputs. In all cases we give sufficient conditions on the connectivity functions for the solutions to be absolutely stable, that is to say independent of the initial state of the field. These conditions bear on some compact operators defined from the connectivity kernels, the sigmoids, and the time constants used in describing the temporal shape of the post-synaptic potentials. Numerical experiments are presented to illustrate the theory. An important contribution of our work is the application of the theory of compact operators in a Hilbert space to the problem of neural fields with the effect of providing very simple mathematical answers to the questions asked by neuroscience modellers

Périgueux – 16 rue du Bac

Lien Atlas (MCC) :http://atlas.patrimoines.culture.fr/atlas/trunk/index.php?ap_theme=DOM_2.01.02&ap_bbox=-0.674;45.174;0.747;45.214 La reconnaissance archéologique du 16 rue du Bac est liée à un projet de construction d’une maison individuelle. Le sondage réalisé (35 m²) a révélé la présence d'un cimetière moderne totalement inédit. Le niveau d'apparition des sépultures se situe environ à 0,75 m de la surface. Le nettoyage rapide de quelques individus a révélé une importante densité de sépult..

Pechbonnieu – Barat

L’intervention fait suite à une demande volontaire de diagnostic préalable à un projet de lotissement. La parcelle, d’un peu plus de 1 ha, correspond à une prairie parsemée de haies et de quelques arbres fruitiers. Les 23 sondages réalisés ont montré la relative proximité du terrain naturel (argile sableuse et/ou molasse apparaissant vers -1 m de profondeur). Le sondage 16 a révélé la présence d’une petite occupation médiévale matérialisée par 4 fosses (silos) et un four de potier. Une sépult..

Sauveterre – Gabanel est

Le diagnostic (500 m2) est lié à la construction d’une piscine, d’un pool house et d’un dispositif d’assainissement toutes eaux. Le projet se situe à proximité immédiate (50 m) du site du Souquet, vaste ensemble antique de plus de 4 000 m2, fouillé partiellement dans les années 1960 et 1990 et dont la fonction exacte reste encore à déterminer (sanctuaire ? villa ? vicus ?). Trois des quatre sondages ont mis en évidence des niveaux de voie, situés entre -1,30 et -1,60 m de profondeur. Deux axe..