A Miniature Robot for Isolating and Tracking Neurons in Extracellular Cortical Recordings

This paper presents a miniature robot device and control algorithm that can autonomously position electrodes in cortical tissue for isolation and tracking of extracellular signals of individual neurons. Autonomous electrode positioning can significantly enhance the efficiency and quality of acute electrophysiolgical experiments aimed at basic understanding of the nervous system. Future miniaturized systems of this sort could also overcome some of the inherent difficulties in estabilishing long-lasting neural interfaces that are needed for practical realization of neural prostheses. The paper describes the robot's design and summarizes the overall structure of the control system that governs the electrode positioning process. We present a new sequential clustering algorithm that is key to improving our system's performance, and which may have other applications in robotics. Experimental results in macaque cortex demonstrate the validity of our approach

A Change Impact Analysis to Characterize Evolving Program Behaviors

Change impact analysis techniques estimate the potential effects of changes made to software. Directed Incremental Symbolic Execution (DiSE) is an intraprocedural technique for characterizing the impact of software changes on program behaviors. DiSE first estimates the impact of the changes on the source code using program slicing techniques, and then uses the impact sets to guide symbolic execution to generate path conditions that characterize impacted program behaviors. DiSE, however, cannot reason about the flow of impact between methods and will fail to generate path conditions for certain impacted program behaviors. In this work, we present iDiSE, an extension to DiSE that performs an interprocedural analysis. iDiSE combines static and dynamic calling context information to efficiently generate impacted program behaviors across calling contexts. Information about impacted program behaviors is useful for testing, verification, and debugging of evolving programs. We present a case-study of our implementation of the iDiSE algorithm to demonstrate its efficiency at computing impacted program behaviors. Traditional notions of coverage are insufficient for characterizing the testing efforts used to validate evolving program behaviors because they do not take into account the impact of changes to the code. In this work we present novel definitions of impacted coverage metrics that are useful for evaluating the testing effort required to test evolving programs. We then describe how the notions of impacted coverage can be used to configure techniques such as DiSE and iDiSE in order to support regression testing related tasks. We also discuss how DiSE and iDiSE can be configured for debugging finding the root cause of errors introduced by changes made to the code. In our empirical evaluation we demonstrate that the configurations of DiSE and iDiSE can be used to support various software maintenance task

Vers des boîtes quantiques à base de graphène

Le graphène est un matériau à base de carbone qui est étudié largement depuis 2004. De très nombreux articles ont été publiés tant sur les propriétés électroniques, qu'optiques ou mécaniques de ce matériel. Cet ouvrage porte sur l'étude des fluctuations de conductance dans le graphène, et sur la fabrication et la caractérisation de nanostructures gravées dans des feuilles de ce cristal 2D. Des mesures de magnétorésistance à basse température ont été faites près du point de neutralité de charge (PNC) ainsi qu'à haute densité électronique. On trouve deux origines aux fluctuations de conductance près du PNC, soit des oscillations mésoscopiques provenant de l'interférence quantique, et des fluctuations dites Hall quantique apparaissant à plus haut champ (>0.5T), semblant suivre les facteurs de remplissage associés aux monocouches de graphène. Ces dernières fluctuations sont attribuées à la charge d'états localisés, et révèlent un précurseur à l'effet Hall quantique, qui lui, ne se manifeste pas avant 2T. On arrive à extraire les paramètres caractérisant l'échantillon à partir de ces données. À la fin de cet ouvrage, on effectue des mesures de transport dans des constrictions et îlots de graphène, où des boîtes quantiques sont formées. À partir de ces mesures, on extrait les paramètres importants de ces boîtes quantiques, comme leur taille et leur énergie de charge

A Control System for Positioning Recording Electrodes to Isolate Neurons in Extracellular Recordings

This thesis presents an algorithm that autonomously positions recording electrodes inside cortical tissue so as to isolate and then maintain optimal extracellular signal recording quality without human intervention. The algorithm is used to improve the quality and efficiency of acute (daily insertion) recordings that are needed for basic research in neurophysiology. It also offers the potential to increase the longevity and quality of chronic (long-term implant) recordings by controlling an emerging class of chronic arrays in which the electrodes can be continually repositioned after implantation. The challenges encountered in attempting to isolate neurons are studied. A solution is proposed in which a finite state machine oversees a number of signal processing steps, computes various metrics of the recording quality and issues commands to move the electrode close to neurons without causing them damage. A number of metrics of the quality of neuron isolation are compared. The algorithm has been used to control a number of commercial microdrive systems, including a single-electrode FHC microdrive and multielectrode microdrives from Thomas Recording and NAN, as well as a novel miniature microdrive. The autonomous positioning software is used by several neuroscientists to perform basic neurophysiology research. Analysis of the system's performance in isolating neurons is included.</p

An Algorithm for Autonomous Isolation of Neurons in Extracellular Recordings

This paper describes novel multi-electrode systems that can autonomously position recording electrodes inside cortical tissue so as to isolate and then maintain optimal extracellular signal recording quality without human intervention. Autonomous microdrives can be used to improve the quality and efficiency of acute recordings that are needed for basic research in neurophysiology. They also offer the potential to increase the longevity and quality of chronic recordings and will serve as the front end of neuroprosthetic systems that aid the handicapped. We first describe the autonomous positioning algorithm, and its implementation as a finite state machine. We have deployed the algorithm on both conventional acute recording micro-drives and a novel miniature robot microdrive. Experimental results in monkey cortex are presented

Estimation de complexité et localisation de véhicules à l'aide de l'apprentissage profond

L'analyse de la circulation routière est un domaine du génie civil permettant d'optimiser le déplacement des véhicules sur un système routier. Une étape importante de tout système d'analyse de trafic routier est la localisation des véhicules. Cette étape est effectuée à l'aide d'algorithmes d'apprentissage automatique, les réseaux de neurones à convolution. Ce mémoire présente deux nouvelles bases de données de localisation et classification de véhicules permettant l'évaluation de techniques d'apprentissage modernes. Celles-ci contiennent plus de 648 959 véhicules classifiés parmi 11 classes. Un atout majeur de la base de données de localisation est la très grande variété de scènes permettant une meilleure évaluation des techniques dans plusieurs contextes différents. Par la suite, on présente une technique d'estimation de la complexité d'une base de données. Cette technique permet d'analyser une base de données en un temps raisonnable et en apprendre plus sur sa composition. Elle permet d'estimer les performances atteignables par un algorithme d'apprentissage automatique sur cette base de données et d'en apprendre plus sur les relations entre les classes. Finalement, une ébauche d'article sur une technique d'apprentissage automatique pour l'estimation d'orientation des véhicules est présentée en annexe. Cette méthode propose l'ajout d'un composant permettant l'«apprentissage en ligne» du modèle ce qui permet d'adapter le modèle à la scène proposée. Malgré cet ajout, ce modèle reste fiable pour la localisation et la classification tout en gardant sa rapidité d'exécution

Development Context Driven Change Awareness and Analysis Framework

Recent work on workspace monitoring allows conflict prediction early in the development process, however, these approaches mostly use syntactic differencing techniques to compare different program versions. In contrast, traditional change-impact analysis techniques analyze related versions of the program only after the code has been checked into the master repository. We propose a novel approach, De- CAF (Development Context Analysis Framework), that leverages the development context to scope a change impact analysis technique. The goal is to characterize the impact of each developer on other developers in the team. There are various client applications such as task prioritization, early conflict detection, and providing advice on testing that can benefit from such a characterization. The DeCAF framework leverages information from the development context to bound the iDiSE change impact analysis technique to analyze only the parts of the code base that are of interest. Bounding the analysis can enable DeCAF to efficiently compute the impact of changes using a combination of program dependence and symbolic execution based approaches