Sequential behaviour in the Rat: Design and applications of a Serial Reaction Time Task

The study of sequential behaviour which relies among others on dopamine mechanisms and basal ganglia networks, is particularly relevant in Parkinsonian patients. Sequential behaviour can be ex- tensively studied through the use of a standard test known as the Serial Reaction Time Task (SRTT) in humans and non-human pri- mates. Although a rodent model of such a test would be very useful to investigate the underlying brain mechanisms of this type of learn- ing, there is no standardised rodent test. The aim of the three studies presented in this work was to characterise sequential behaviour in the intact rat as an analogy to the human standard test. The aim of the first study was to implement a rat model of the hu- man standard SRTT. The designed task required the rats to poke fast with their nose (motor answer) into lit holes (visual stimulus, one of four locations) and to perform a series of such nosepokes in order to get a food-reward, according to a fixed ratio schedule of reinforce- ment (FR). The location of the light was displayed in either random or sequential order and sequential learning was inferred from the dif- ference in performance between the two conditions within-session. We found that the rats performed better in the sequential condition, in terms of speed, accuracy and number of rewards earned. Details of the test were improved in the course of the studies to ensure that the better performance in sequential condition could only be attributed to the learning of the serial order information and no other general skill. Rats were finally tested on a repeated sequence of twelve ordered locations under a FR13. The length of the FR13 series was intention- ally longer than the length of the repeated sequence to dissociate the sequence locations from the FR schedule positions. The sequence structure was cautiously generated according to statistical rules (e.g. locations frequency, transitions frequency). These features provided a level of sequence difficulty comparable to the human one. This test was used in the second study to investigate the role of dopamine in this task in general and in the sequential performance of well-trained rats in particular. As this SRTT was planned to be applied in dopamine-depleted rats, the effects of the blockade of the dopaminergic transmission were first studied. A D1 and a D2 se- lective antagonists were used and injected systemically. We found that both antagonists produced dramatic disruption of responding, decreased response rate and increased the number of omissions. Only the D1 antagonist increased accuracy to a small extent. These effects were independent of the condition and dose-dependent. The D1 antagonist specifically impaired initial reaction times (within the first halves) of the series, whereas the D2 antagonist affected the whole pattern. Under D1 antagonist treatment, reaction times did not improve in sequential condition compared to random condition, which would reflect a specific effect of the D1 receptor in sequential performance. The third study aimed at investigating to which extent well-trained rats in the SRTT developed a habit. Rats were trained in sequential con- dition and were then confronted during a test with randomly inserted unique sequence violations. A detailed analysis of the performance yielded that rats showed indices of habit but also that attention was still playing a role. At the position of the violation, either the rats dis- played lengthened reaction times for correct pokes or poked fast into the hole where the light should have appeared according to the se- quential order (“expected” light location). This fast answer was how- ever now incorrect because of the sequence violation. Repetition of this test in a bigger group of rats proved the reliability of these results. In this repeated experiment, the apparatus and details of the task (but not of the sequence) were modified to suit application in dopamine- depleted animals for which motor requirements for example, have to be minimized. The rat SRTT with food-reinforcement described here shows high face-validity with the standard human SRTT. It has been effective for the biopsychological characterisation in intact rats of sequential performance, which in many aspects resembled the human one. The designed SRTT with food-reinforcement will probably be of value as a rodent model for the study of sequential behaviour in dopamine- depleted animals as a model for Parkinson disease

Adaptive SLAM with synthetic stereo dataset generation for real-time dense 3D reconstruction

International audienceIn robotic mapping and navigation, of prime importance today with the trend for autonomous cars, simultaneous localization and mapping (SLAM) algorithms often use stereo vision to extract 3D information of the surrounding world. Whereas the number of creative methods for stereo-based SLAM is continuously increasing, the variety of datasets is relatively poor and the size of their contents relatively small. This size issue is increasingly problematic, with the recent explosion of deep learning based approaches, several methods require an important amount of data. Those multiple techniques contribute to enhance the precision of both localization estimation and mapping estimation to a point where the accuracy of the sensors used to get the ground truth might be questioned. Finally, because today most of these technologies are embedded on on-board systems, the power consumption and real-time constraints turn to be key requirements. Our contribution is twofold: we propose an adaptive SLAM method that reduces the number of processed frame with minimum impact error, and we make available a synthetic flexible stereo dataset with absolute ground truth, which allows to run new benchmarks for visual odometry challenges. This dataset is available online at http://alastor.labri.fr/

Striatal neuropeptides enhance selection and rejection of sequential actions

The striatum is the primary input nucleus for the basal ganglia, and receives glutamatergic afferents from the cortex. Under the hypothesis that basal ganglia perform action selection, these cortical afferents encode potential “action requests.” Previous studies have suggested the striatum may utilize a mutually inhibitory network of medium spiny neurons (MSNs) to filter these requests so that only those of high salience are selected. However, the mechanisms enabling the striatum to perform clean, rapid switching between distinct actions that form part of a learned action sequence are still poorly understood. Substance P (SP) and enkephalin are neuropeptides co-released with GABA in MSNs preferentially expressing D1 or D2 dopamine receptors respectively. SP has a facilitatory effect on subsequent glutamatergic inputs to target MSNs, while enkephalin has an inhibitory effect. Blocking the action of SP in the striatum is also known to affect behavioral transitions. We constructed phenomenological models of the effects of SP and enkephalin, and integrated these into a hybrid model of basal ganglia comprising a spiking striatal microcircuit and rate–coded populations representing other major structures. We demonstrated that diffuse neuropeptide connectivity enhanced the selection of unordered action requests, and that for true action sequences, where action semantics define a fixed structure, a patterning of the SP connectivity reflecting this ordering enhanced selection of actions presented in the correct sequential order and suppressed incorrect ordering. We also showed that selective pruning of SP connections allowed context–sensitive inhibition of specific undesirable requests that otherwise interfered with selection of an action group. Our model suggests that the interaction of SP and enkephalin enhances the contrast between selection and rejection of action requests, and that patterned SP connectivity in the striatum allows the “chunking” of actions and improves selection of sequences. Efficient execution of action sequences may therefore result from a combination of ordered cortical inputs and patterned neuropeptide connectivity within striatum

Perceptually-guided deep neural networks for ego-action prediction: Object grasping

We tackle the problem of predicting a grasping action in ego-centric video for the assistance to upper limb amputees. Our work is based on paradigms of neuroscience that state that human gaze expresses intention and anticipates actions. In our scenario, human gaze fixations are recorded by a glass-worn eye-tracker and then used to predict the grasping actions. We have studied two aspects of the problem: which object from a given taxonomy will be grasped, and when is the moment to trigger the grasping action. To recognize objects, we using gaze to guide Convolutional Neural Networks (CNN) to focus on an object-to-grasp area. However, the acquired sequence of fixations is noisy due to saccades toward distractors and visual fatigue, and gaze is not always reliably directed toward the object-of-interest. To deal with this challenge, we use video-level annotations indicating the object to be grasped and a weak loss in Deep CNNs. To detect a moment when a person will take an object we take advantage of the predictive power of Long-Short Term Memory networks to analyze gaze and visual dynamics. Results show that our method achieves better performance than other approaches on a real-life dataset. (C) 2018 Elsevier Ltd. All rights reserved.This work was partially supported by French National Center of Scientific research with grant Suvipp PEPS CNRS-Idex 215-2016, by French National Center of Scientific research with Interdisciplinary project CNRS RoBioVis 2017–2019, the Scientific Council of Labri, University of Bordeaux, and the Spanish Ministry of Economy and Competitiveness under the National Grants TEC2014-53390-P and TEC2014-61729-EXP.Publicad

Mise en place d'une chaîne complète d'analyse de l'arbre trachéo-bronchique à partir d'examen(s) issus d'un scanner-CT (de la 3D vers la 4D)

Afin de répondre au problème de santé publique que représente l'asthme, l'imagerie tomodensitométrique associé aux traitements informatiques permettent la quantification et le suivi des dommages subis par les bronches. Le but de l'imagerie bronchique, lors d'un examen de type scanner-CT est de disposer de mesures fiables et reproductibles des différents paramètres bronchiques qui sont des marqueurs de l'importance de la pathologie et de son évolution sous traitements. Ces marqueurs correspondent à deux mesures LA ( Lumen Area) et WA ( Wall Area) prises sur des coupes perpendiculaires à la bronche. La mise en place d'une chaîne de traitements constitué de maillons d'extraction et de squelettisation de l'arbre trachéo-bronchique permet l'obtention de tels mesures. Durant cette thèse nous nous sommes focalisés sur la création d'une chaîne de traitements en proposant une contribution sur chacun des maillons. Notre chaîne est modulable et adaptée au travail en 4D (différentes phases respiratoires) et à fait l'objet d'une implémentation logiciel intitulée Neko4D.[Abstract not provided]BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Function and Comorbidities of Apolipoprotein E in Alzheimer's Disease

Alzheimer's disease (AD)—the most common type of dementia among the elderly—represents one of the most challenging and urgent medical mysteries affecting our aging population. Although dominant inherited mutation in genes involved in the amyloid metabolism can elicit familial AD, the overwhelming majority of AD cases, dubbed sporadic AD, do not display this Mendelian inheritance pattern. Apolipoprotein E (APOE), the main lipid carrier protein in the central nervous system, is the only gene that has been robustly and consistently associated with AD risk. The purpose of the current paper is thus to highlight the pleiotropic roles and the structure-function relationship of APOE to stimulate both the functional characterization and the identification of novel lipid homeostasis-related molecular targets involved in AD

Évaluation de la qualité des documents anciens numérisés

Les travaux de recherche présentés dans ce manuscrit décrivent plusieurs apports au thème de l évaluation de la qualité d images de documents numérisés. Pour cela nous proposons de nouveaux descripteurs permettant de quantifier les dégradations les plus couramment rencontrées sur les images de documents numérisés. Nous proposons également une méthodologie s appuyant sur le calcul de ces descripteurs et permettant de prédire les performances d algorithmes de traitement et d analyse d images de documents. Les descripteurs sont définis en analysant l influence des dégradations sur les performances de différents algorithmes, puis utilisés pour créer des modèles de prédiction à l aide de régresseurs statistiques. La pertinence, des descripteurs proposés et de la méthodologie de prédiction, est validée de plusieurs façons. Premièrement, par la prédiction des performances de onze algorithmes de binarisation. Deuxièmement par la création d un processus automatique de sélection de l algorithme de binarisation le plus performant pour chaque image. Puis pour finir, par la prédiction des performances de deux OCRs en fonction de l importance du défaut de transparence (diffusion de l encre du recto sur le verso d un document). Ce travail sur la prédiction des performances d algorithmes est aussi l occasion d aborder les problèmes scientifiques liés à la création de vérités-terrains et d évaluation de performances.This PhD. thesis deals with quality evaluation of digitized document images. In order to measure the quality of a document image, we propose to create new features dedicated to the characterization of most commons degradations. We also propose to use these features to create prediction models able to predict the performances of different types of document analysis algorithms. The features are defined by analyzing the impact of a specific degradation on the results of an algorithm and then used to create statistical regressors.The relevance of the proposed features and predictions models, is analyzed in several experimentations. The first one aims to predict the performance of different binarization methods. The second experiment aims to create an automatic procedure able to select the best binarization method for each image. At last, the third experiment aims to create a prediction model for two commonly used OCRs. This work on performance prediction algorithms is also an opportunity to discuss the scientific problems of creating ground-truth for performance evaluation.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Recherche de motifs quasi-similaires dans des graphes

National audienceNous décrivons un algorithme basé sur des métriques intrinsèques de graphes permettant de découvrir des motifs communs et similaires entre plu- sieurs graphes. Nous montrons des applications à la recherche d?image dans une collection et à l?interprétation de données géographique

A heuristic for the retrieval of objects in low resolution video

International audienceIn this paper, we tackle the problem of matching of objects in video in the context of the rough indexing paradigm. In this context, the video data are of very low resolution and segmentation is consequently inaccurate. The region features (texture, color, shape) are not strongly relevant due to the resolution. The structure of the objects must be considered in order to improve the robustness of the matching of regions. Indeed, the problem of object matching can be expressed in terms of directed acyclic graph (DAG) matching. Here, we propose a method based on a heuristic in order to approach object matching. The results are compared with those of a method based on relaxation matching