Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development

A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks

Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care

Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies

Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies

Hybridation des réseaux de neurones : De la conception du réseau à l'interopérabilité des systèmes neuromorphiques

HYBRID experiments allow to connect a biological neural network with an artificial one,used in neuroscience research and therapeutic purposes. During these three yearsof PhD, this thesis focused on hybridization in a close-up view (bi-diretionnal directcommunication between the artificial and the living) and in a broader view (interoperability ofneuromorphic systems).In the early 2000s, an analog neuromorphic system has been connected to a biological neuralnetwork. This work is firstly, about the design of a digital neural network, for hybrid experimentsin two multi-disciplinary projects underway in AS2N team of IMS presented in this document : HYRENE (ANR 2010-Blan-031601), aiming the development of a hybrid system for therestoration of motor activity in the case of a spinal cord lesion, BRAINBOW (European project FP7-ICT-2011-C), aiming the development of innovativeneuro-prostheses that can restore communication around cortical lesions.Having a configurable architecture, a digital neural network was designed for these twoprojects. For the first project, the artificial neural network emulates the activity of CPGs (CentralPattern Generator), causing the locomotion in the animal kingdom. This activity will trigger aseries of stimuli in the injured spinal cord textit in vitro and recreating locomotion previouslylost. In the second project, the neural network topology will be determined by the analysis anddecryption of biological signals from groups of neurons grown on electrodes, as well as modelingand simulations performed by our partners. The neural network will be able to repair the injuredneural network.This work show the two different networks design approach and preliminary results obtainedin the two projects.Secondly, this work hybridization to extend the interoperability of neuromorphic systems.Through a communication protocol using Ethernet, it is possible to interconnect electronic neuralnetworks, computer and biological. In the near future, it will increase the complexity and size ofnetworks.L’hybridation est une technique qui consiste à interconnecter un réseau de neurones biologiqueet un réseau de neurones artificiel, utilisée dans la recherche en neuroscience età des fins thérapeutiques. Durant ces trois années de doctorat, ce travail de thèse s’estfocalisé sur l’hybridation dans un plan rapproché (communication directe bi-diretionnelle entrel’artificiel et le vivant) et dans un plan plus élargies (interopérabilité des systèmes neuromorphiques).Au début des années 2000, cette technique a permis de connecter un système neuromorphiqueanalogique avec le vivant. Ce travail est dans un premier temps, centré autour de la conceptiond’un réseau de neurones numérique, en vue d’hybridation, dans deux projets multi-disciplinairesen cours dans l’équipe AS2N de l’IMS, présentés dans ce document : HYRENE (ANR 2010-Blan-031601), ayant pour but le développement d’un systèmehybride de restauration de l’activité motrice dans le cas d’une lésion de la moelle épinière, BRAINBOW (European project FP7-ICT-2011-C), ayant pour objectif l’élaboration deneuro-prothèses innovantes capables de restaurer la communication autour de lésionscérébrales.Possédant une architecture configurable, un réseau de neurones numérique a été réalisé pources deux projets. Pour le premier projet, le réseau de neurones artificiel permet d’émuler l’activitéde CPGs (Central Pattern Generator), à l’origine de la locomotion dans le règne animale. Cetteactivité permet de déclencher une série de stimulations dans la moelle épinière lésée in vitro et derecréer ainsi la locomotion précédemment perdue. Dans le second projet, la topologie du réseaude neurones sera issue de l’analyse et le décryptage des signaux biologiques issues de groupesde neurones cultivés sur des électrodes, ainsi que de modélisations et simulations réalisées parnos partenaires. Le réseau de neurones sera alors capable de réparer le réseau de neurones lésé.Ces travaux de thèse présentent la démarche de conception des deux différents réseaux et desrésultats préliminaires obtenus au sein des deux projets.Dans un second temps, ces travaux élargissent l’hybridation à l’interopérabilité des systèmesneuromorphiques. Au travers d’un protocole de communication utilisant Ethernet, il est possibled’interconnecter des réseaux de neurones électroniques, informatiques et biologiques

Méthodes statistiques pour la prise en compte de différentes sources de biais dans les études d'association à grande échelle

Les études d'association à grande échelle sont devenus un outil très performant pour détecter les variants génétiques associés aux maladies. Ce manuscrit de doctorat s'intéresse à plusieurs des aspects clés des nouvelles problématiques informatiques et statistiques qui ont émergé grâce à de telles recherches. Les résultats des études d'association à grande échelle sont critiqués, en partie, à cause du biais induit par la stratification des populations. Nous proposons une étude de comparaison des stratégies qui existent pour prendre en compte ce problème. Leurs avantages et limites sont discutés en s'appuyant sur divers scénarios de structure des populations dans le but de proposer des conseils et indications pratiques. Nous nous intéressons ensuite à l'interférence de la structure des populations dans la recherche génétique. Nous avons développé au cours de cette thèse un nouvel algorithme appelé SHIPS (Spectral Hierarchical clustering for the Inference of Population Structure). Cet algorithme a été appliqué à un ensemble de jeux de données simulés et réels, ainsi que de nombreux autres algorithmes utilisés en pratique à titre de comparaison. Enfin, la question du test multiple dans ces études d'association est abordée à plusieurs niveaux. Nous proposons une présentation générale des méthodes de tests multiples et discutons leur validité pour différents designs d'études. Nous nous concertons ensuite sur l'obtention de résultats interprétables aux niveaux de gènes, ce qui correspond à une problématique de tests multiples avec des tests dépendants. Nous discutons et analysons les différentes approches dédiées à cette fin.Genome-Wide association studies have become powerful tools to detect genetic variants associated with diseases. This PhD thesis focuses on several key aspects of the new computational and methodological problematics that have arisen with such research. The results of Genome-Wide association studies have been questioned, in part because of the bias induced by population stratification. Many stratégies are available to account for population stratification scenarios are highlighted in order to propose pratical guidelines to account for population stratification. We then focus on the inference of population structure that has many applications for genetic research. We have developed and present in this manuscript a new clustering algoritm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS). This algorithm in the field to propose a comparison of their performances. Finally, the issue of multiple-testing in Genome-Wide association studies is discussed on several levels. We propose a review of the multiple-testing corrections and discuss their validity for different study settings. We then focus on deriving gene-wise interpretation of the findings that corresponds to multiple-stategy to obtain valid gene-disease association measures.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns

Discriminative Classification vs Modeling Methods in CBIR

Statistical learning methods are currently considered with an increasing interest in the content-based image retrieval (CBIR) community. We compare in this article two leader techniques for classification tasks. The first method uses one-class and two-class SVM to discriminate data. The second approach is based on Gaussian Mixture to model classes. To deal with the specificity of the CBIR classification task, adaptations have been proposed. Experimental tests on a generalist database have been carried out. Advantages and drawbacks are discussed for each method

Méthodes d'Apprentissage pour la Recherche d'Images par le Contenu.

SVM et Mélanges de Gaussiennes pour la recherche d'image

Prospective assessment of reproducibility of three-dimensional ultrasound for fetal biometry

International audiencePurpose: To compare fetal ultrasound measurements performed by two observers with different levels of experience and evaluate the potential contribution of the use of three-dimensional (3D) ultrasound on repeatability, reproducibility and agreement of two-dimensional (2D) and 3D-derived measurements. Materials and methods: Two observers (one senior and one junior) measured head circumference (HC), abdominal circumference (AC) and femur length (FL) in 33 fetuses (20 to 40 weeks of gestation). Each observer performed two series of 2D measurements and two series of 3D measurements (i.e., measurements derived from triplane volume processing). Measurements were converted into Z-scores according to gestational age. Variability between the different series of measurements was studied using Bland–Altmann plots and intra-class correlation coefficients (ICC). Results: Agreement with the 2D measurements of the senior observer was higher in 3D than in 2D for the junior observer (systematic differences of −0.4, −0.2 and −0.8 Z-score vs. −0.1, −0.1 and −0.6 for HC, AC and FL on 2D and 3D datasets, respectively). The use of 3D ultrasound improved junior observer repeatability (ICC = 0.94, 0.88, 0.90 vs. 0.94, 0.94 and 0.96 for HC, AC and FL in 2D and 3D, respectively). The reproducibility was greater using the junior observer 3D datasets (ICC = 0.75, 0.60 and 0.45 vs. 0.79, 0.89 and 0.63 for HC, AC and FL, respectively). Conclusion: The use of 3D ultrasound improves the consistency of the measurements performed by a junior observer and increases the overall repeatability and reproducibility of measurements performed by observers with different levels of experience