Дисертації з теми "Graphene neurons"
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CONVERTINO, Domenica. "Interfacing graphene with peripheral neurons: influence of neurite outgrowth and NGF axonal transport." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/90468.
Повний текст джерелаVeliev, Farida. "Interfacing neurons with nanoelectronics : from silicon nanowires to carbon devices." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAI001/document.
Повний текст джерелаIn line with the technological progress of last decades a variety of adapted bioelectrical interfaces was developed to record electrical activity from the nervous system reaching from whole brain activity to single neuron signaling. Although neural interfaces have reached clinical utility and are commonly used in fundamental neuroscience, their performance is still limited. In this work we investigated alternative materials and techniques, which could improve the monitoring of neuronal activity of cultured networks, and the long-term performance of prospective neuroprosthetics. While silicon nanowire transistor arrays and diamond based microelectrodes are proposed for improving the spatial resolution and the electrode stability in biological environment respectively, the main focus of this thesis is set on the evaluation of graphene based field effect transistor arrays for bioelectronics. Due to its outstanding electrical, mechanical and chemical properties graphene appears as a promising candidate for the realization of chemically stable flexible electronics required for long-term neural interfacing. Here we demonstrate the outstanding neural affinity of pristine graphene and the realization of highly sensitive fast graphene transistors for neural interfaces
Turco, Antonio. "Use of carbon nanotubes for novel approaches towards spinal network repairing." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8663.
Повний текст джерелаNanotechnology underwent a very rapid development in the last decades, thanks to the invention of different techniques that allow reaching the nanoscale. The great interest in this area arises from the variety of possible applications in different fields, such as electronics, where the miniaturization of components is a key factor, but also medicine. The creation of smart systems able to carry out a specific task in the body in a controlled way, either in diagnosis or therapy or tissue engineering, is the ultimate goal of a newborn area of research, called nanomedicine. In fact, to reach such an outstanding objective, a nanometer‐sized material is needed and carbon nanotubes (CNTs) are among the most promising candidates. The aim of this thesis was to study this opportunity and, in particular, the possible application of carbon nanotubes for spinal network repairing. After a review of the main features of neuronal network systems and the most common techniques to study their functionality, possible applications of nanotechnology for nanomedicine purposes are considered, focusing the attention on CNTs as neuronal interface in nerve tissue engineering. The work can be divided into two big parts. In the first part the impact of carbon nanotubes on various neuronal systems was studied. Different form of carbonaceous materials (carbon nanotubes, nanohorns and graphene) were deposited in a homogeneous way on a glass surface playing with organic functionalization and different deposition techniques. Hippocampal neuronal cells were grown on their surface to better understand how morphology and conductivity of the material could influence the activity of the neuronal network evidencing how both these characteristics could affect the electrophysiological properties of neurons. Then, also spinal neurons were grown on carbon nanotubes network deposited on a glass substrate to evaluate, for the first time, the impact of carbon nanotubes on this kind of cells. The tight interaction between these two materials appeared to cause a faster maturation of the spinal neurons with respect II to the control grown on a glass substrate. The long-term impact on a complex tissue (spinal cord slice) grown on carbon nanotubes carpet was also studied. The intimate interaction between the two materials observed by TEM and SEM analysis caused an increase in dimensions and number of neuronal fibers that comes out from the body of a spinal cord slice. An increase in electrophysiological activity of all neuronal network of the slice was also reported. In the second part of the work different conductive biocompatible nanocomposite materials based on carbon nanotubes and “artificial” polymers (such as Nafion, PVA, PET, PEI, PDMS and PANI) were investigated. The idea is to test these materials as neuronal prosthesis to repair spinal cord damage. All the prepared scaffolds showed CNTs on the surface favoring CNTs-neurons interaction. To address this aim different techniques and different organic functionalizations of CNTs were utilized to control supramolecular interactions between the nanomaterial and polymers orienting the deposition of the CNTs and preventing their aggregation. After that, an innovative method to study the possible ability of this nanocomposite materials to transmit a neuronal signal between two portions of spinal cord was designed. Functionalization of gold surfaces with thiolated carbon nanotubes have been conducted in order to develop suitable devices for neuronal stimulation and consequent spinal cord lesions repairing. In particular thiol groups were introduced on the graphitic surface of carbon nanotubes by means of covalent functionalization. First of all, the interaction of CNTs with gold nanoparticles has been evaluated, then a gold surface has been coated by means of contact printing technique with a homogeneous film of CNTs. This hybrid material could be useful to produce innovative electrodes for neuronal stimulation
XXV Ciclo
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Bourrier, Antoine. "Bioélectronique graphène pour un interfaçage neuronal in-vivo durable." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAY011/document.
Повний текст джерелаGraphene, an atomically thin layer of carbon, is investigated as a biosensing andcoating material in order to address the long term durability issues of invasive intracorticalimplants. These devices are essential tools to record specific single motor neurons activity formedical applications aiming at healing neural injuries. Today’s implants suffer from their highinvasiveness. It is responsible for local inflammation that leads to the failure in unique neuronsactivity recordings in the motor cortex on a long term basis. By combining a monolayergraphene growth and transfer with an ultra-sensitive electronic integration and a biochemicalfunctionalization, this thesis proposes a new multidisciplinary approach to build intracorticalimplants with an improved bioacceptance. By using innovative methods of grapheneintegration in implants, and in-vitro and in-vivo studies to assess the reactions of living tissuesto graphene, we provide an overview of graphene’s potential contribution to future brainmachine interfaces for long term medical projects
Viana, Casals Damià. "EGNITE: Engineered Graphene for Neural Interface." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673330.
Повний текст джерелаLa tecnología de implantes neuronales en medicina tiene como objetivo restaurar la funcionalidad del sistema nervioso en casos de degeneración o daño grave registrando o estimulando la actividad eléctrica del tejido nervioso. Los implantes neurales disponibles actualmente ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantean los metales utilizados en la interfaz eléctrica con el tejido. Dichos materiales comprometen la resolución de la interfaz y, por lo tanto, la restauración funcional con el rendimiento y la estabilidad. En este trabajo presento unos implantes neuronales flexibles basados en una película delgada de grafeno poroso nanoestructurado y biocompatible que proporciona una interfaz neural bidireccional estable y de alto rendimiento. En comparación con los dispositivos de microelectrodos de platino estándar, electrodos de 25 μm de diámetro basados en grafeno ofrecen una impedancia significativamente menor y pueden inyectar de forma segura 200 veces más carga durante más de 100 millones de pulsos. Aquí evaluo sus capacidades in vivo registrando actividad epicortical con alta fidelidad y alta resolución, estimulando subconjuntos de axones dentro del nervio ciático con umbrales de corriente bajos y alta selectividad y modulando la actividad de la retina con alta precisión. La tecnología de película fina de grafeno aquí descrita tiene el potencial de convertirse en el nuevo punto de referencia para la próxima generación de tecnología de implantes neuronales.
Neural implants technology in medicine aims to restore nervous system functionality in cases of severe degeneration or damage by recording or stimulating the electrical activity of the nervous tissue. Currently available neural implants offer a modest clinical efficacy partly due to the limitations posed by the metals used at the electrical interface with the tissue. Such materials compromise interfacing resolution, and therefore functional restoration, with performance and stability. In this work, I present flexible neural implants based on a biocompatible nanostructured porous graphene thin film that provides a stable and high performance bidirectional neural interface. Compared to standard platinum microelectrode devices, the graphene-based electrodes of 25 μm diameter offer significantly lower impedance and can safely inject 200 times more charge for more than 100 million pulses. I assessed their performance in vivo by recording high fidelity and high resolution epicortical activity, by stimulating subsets of axons within the sciatic nerve with low thresholds and high selectivity and by modulating the retinal activity with high precision. The graphene thin film technology I describe here has the potential to become the new performance benchmark for the next generation of neural implant technology.
Universitat Autònoma de Barcelona. Programa de Doctorat en Enginyeria Electrònica i de Telecomunicació
Bonaccini, Calia Andrea. "Graphene field-effect transistors as flexible neural interfaces for intracortical electrophysiology." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671635.
Повний текст джерелаEn los últimos años se han producido nuevos desarrollos tecnológicos en el campo de los implantes neuronales para aplicaciones médicas. La comprensión del cerebro humano se considera uno de los mayores desafíos científicos de nuestro tiempo; como consecuencia, estamos siendo testigos de una intensificación de la investigación en el desarrollo de las interfaces cerebro-máquina (IMC) para leer y estimular la actividad cerebral. No obstante, los implantes neuronales actualmente disponibles ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantea la invasividad de los materiales. Esos materiales comprometen la resolución de la interfaz, el rendimiento y la estabilidad a largo plazo de los implantes neurales. El desarrollo de una electrónica flexible que utilice materiales biocompatibles es clave para la realización de implantes neuronales mínimamente invasivos que puedan implantarse de forma crónica. Un campo de investigación muy prometedor es el uso de materiales bidimensionales, como el grafeno, para aplicaciones bioelectrónicas. El transistor de efecto de campo en solución de grafeno (gSGFET) es una de dichas nuevas tecnologías neurales emergentes. Estos dispositivos pueden superar las limitaciones mencionadas anteriormente gracias a las extraordinarias propiedades del grafeno, como su alta flexibilidad mecánica, estabilidad electroquímica, biocompatibilidad y sensibilidad. En esta tesis doctoral, se han fabricado matrices de gSGFET y se han optimizado iterativamente en términos de sensibilidad y relación señal/ruido, adoptando métodos de microfabricación a escala de oblea. Se ha caracterizado el ruido 1/f en los gSGFETs y optimizado haciendo un tratamiento UVO en la interfaz metal/grafeno y desacoplando el canal de grafeno del sustrato utilizando diferentes nanomateriales como la encapsulación con nitruro de boro hexagonal (hBN), monocapas autoensambladas y bicapas de grafeno. Además, se han fabricado con éxito sondas neurales epicorticales e intracorticales flexibles con matrices de gSGFET y se han utilizado durante las medidas de microelectrocorticografía in vivo en roedores. Se han insertado dispositivos intracorticales flexibles en el cerebro utilizando un protocolo de refuerzo de la capa posterior de los dispositivos con proteína de fibroína de seda biorresistente. Los resultados presentados en esta tesis demuestran la superior resolución espacio-temporal de los gSGFET en comparación con la tecnología estándar de microelectrodos; en particular, referente a la capacidad de mapear con alta fidelidad, la actividad de muy baja frecuencia (ISA, < 0,1 Hz) junto con las señales en el típico ancho de banda LFP. Hoy en día se sabe que la actividad cerebral de muy baja frecuencia, contribuye a la fisiopatología de varios trastornos neurológicos como el derrame cerebral, la lesión cerebral traumática, la migraña y la epilepsia. Sin embargo, esta actividad rara vez se registra debido a las limitaciones técnicas intrínsecas de los electrodos convencionales acoplados a la CA. Se han obtenido registros con sondas neuronales de profundidad de grafeno (gDNP) en modelos animales de epilepsia. Se detectó ISA a través de diferentes capas corticales y regiones subcorticales, registrando simultáneamente la actividad epiléptica en bandas de frecuencia más convencionales (1-600Hz). Además, se ha demostrado también la evaluación de la estabilidad y funcionalidad en registros crónicos, así como la biocompatibilidad del gDNP. La tecnología bioelectrónica basada en el grafeno aquí descrita tiene el potencial de convertirse en una herramienta de referencia para la electrofisiología de ancho de banda completo. Se prevé que esta tecnología tenga un gran impacto en una comunidad amplia y multidisciplinaria que incluya investigadores en neurotecnología, ingenieros biomédicos, neurocientíficos que estudien la dinámica cortical de banda ancha asociada con el comportamiento espontáneo y/o los estados cerebrales, así como investigadores clínicos interesados en la actividad de baja frecuencia en la epilepsia, los accidentes cerebrovasculares y la migraña.
Recent years have witnessed novel technology developments of neural implants for medical applications which are expected to pave the way to unveil functionalities of the central nervous system. Understanding the human brain is commonly considered one of the biggest scientific challenges of our time; as a consequence, we are witnessing an intensified research in the development of brain-machine-interfaces (BMIs), which would allow us to both read and stimulate brain activity. Nevertheless, currently available neural implants offer a modest clinical efficacy, partly due to the limitations posed by the invasiveness of the implants materials and technology and by the metals used at the electrical interface with the tissue. Such materials compromise the interfacing resolution, the performance and the long term stability of neural implants. Development of flexible electronics using biocompatible materials is key for the realisation of minimally invasive neural implants, which can be chronically implanted without causing rejection from the immune system. A relatively young yet very promising research field, that is increasingly drawing attention is the use of two dimensional materials, such as graphene, for bioelectronic applications. Graphene solution-gated field effect transistor (gSGFET) is one of several emerging new neural technologies. These devices can overcome the above-mentioned limitations thanks to the outstanding properties of graphene, such as mechanical flexibility, electrochemical inertness, biocompatibility and high sensitivity. In this PhD thesis, arrays of gSGFETs have been fabricated and iteratively optimized in terms of sensitivity and signal-to-noise ratio, adopting wafer-scale micro-fabrication methods. The 1/f noise in gSGFETs has been characterised and the optimisation of both, contact and channel noises was achieved by UVO-treatment at the metal/graphene interface, as well as by decoupling the graphene channel from the substrate, using different nanomaterials such as graphene encapsulation with hexagonal boron nitride (hBN), self assembled monolayers and double transferred graphene. Moreover, flexible and ultra-thin epicortical and intracortical neural probes, containing arrays of gSGFETs, have been successfully fabricated and used during in vivo microelectrocorticography recordings in anaesthesized and awake rodents. Flexible intracortical devices were inserted into the brain using a back-coating stiffening protocol with bioresobable silk fibroin protein, developed during this PhD thesis. The results presented in this PhD demonstrate the superior spatio-temporal resolution of gSGFETs compared to standard microelectordes technology; particularly the ability to map with high fidelity, infraslow activity (ISA, < 0.1 Hz) together with signals in the typical local field potential bandwidth. Today it is known that infraslow brain activity, including spreading depolarisations, contribute to the pathophysiology of several neurological disorders such as stroke, traumatic brain injury, migraine and epilepsy. However, this activity is seldom recorded due to intrinsic technical limitations of conventional AC-coupled electrodes. To demonstrate the usefulness of the developed flexible gSGFET arrays technology, recordings have been obtained with multichannel flexible graphene depth neural probes (gDNP) in relevant awake animal models of seizures and established epilepsy. ISA was detected and mapped through different cortical layers and subcortical regions, whilst simultaneously recording epileptiform activity in more conventional frequency bands (1-600Hz). Furthermore, the assessment of the long term recording stability and functionality, as well as biocompatibility of the gDNP has also been demonstrated as part of this thesis. The graphene based bioelectronic technology here described has the potential to become a gold standard tool for full bandwidth electrophysiology. This technology is envisioned to have a great impact on a broad and multidisciplinary community including neurotechnology researchers, biomedical engineers, neuroscientists studying wide-band cortical dynamics associated with spontaneous behaviour and/or brain states, as well as clinical researchers interested in the role of infraslow activity in epilepsy, stroke and migraine.
EL, MERHIE AMIRA. "Single Layer Graphene Biointerface: Studying Neuronal Network Development and Monitoring Cell Behavior over Time." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/939896.
Повний текст джерелаLimnios, Stratis. "Graph Degeneracy Studies for Advanced Learning Methods on Graphs and Theoretical Results Edge degeneracy: Algorithmic and structural results Degeneracy Hierarchy Generator and Efficient Connectivity Degeneracy Algorithm A Degeneracy Framework for Graph Similarity Hcore-Init: Neural Network Initialization based on Graph Degeneracy." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX038.
Повний текст джерелаExtracting Meaningful substructures from graphs has always been a key part in graph studies. In machine learning frameworks, supervised or unsupervised, as well as in theoretical graph analysis, finding dense subgraphs and specific decompositions is primordial in many social and biological applications among many others.In this thesis we aim at studying graph degeneracy, starting from a theoretical point of view, and building upon our results to find the most suited decompositions for the tasks at hand.Hence the first part of the thesis we work on structural results in graphs with bounded edge admissibility, proving that such graphs can be reconstructed by aggregating graphs with almost-bounded-edge-degree. We also provide computational complexity guarantees for the different degeneracy decompositions, i.e. if they are NP-complete or polynomial, depending on the length of the paths on which the given degeneracy is defined.In the second part we unify the degeneracy and admissibility frameworks based on degree and connectivity. Within those frameworks we pick the most expressive, on the one hand, and computationally efficient on the other hand, namely the 1-edge-connectivity degeneracy, to experiment on standard degeneracy tasks, such as finding influential spreaders.Following the previous results that proved to perform poorly we go back to using the k-core but plugging it in a supervised framework, i.e. graph kernels. Thus providing a general framework named core-kernel, we use the k-core decomposition as a preprocessing step for the kernel and apply the latter on every subgraph obtained by the decomposition for comparison. We are able to achieve state-of-the-art performance on graph classification for a small computational cost trade-off.Finally we design a novel degree degeneracy framework for hypergraphs and simultaneously on bipartite graphs as they are hypergraphs incidence graph. This decomposition is then applied directly to pretrained neural network architectures as they induce bipartite graphs and use the coreness of the neurons to re-initialize the neural network weights. This framework not only outperforms state-of-the-art initialization techniques but is also applicable to any pair of layers convolutional and linear thus being applicable however needed to any type of architecture
Albano, Alice. "Dynamique des graphes de terrain : analyse en temps intrinsèque." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066260/document.
Повний текст джерелаWe are surrounded by a multitude of interaction networks from different contexts. These networks can be modeled as graphs, called complex networks. They have a community structure, i.e. groups of nodes closely related to each other and less connected with the rest of the graph. An other phenomenon studied in complex networks in many contexts is diffusion. The spread of a disease is an example of diffusion. These phenomena are dynamic and depend on an important parameter, which is often little studied: the time scale in which they are observed. According to the chosen scale, the graph dynamics can vary significantly. In this thesis, we propose to study dynamic processes using a suitable time scale. We consider a notion of relative time which we call intrinsic time, opposed to "traditional" time, which we call extrinsic time. We first study diffusion phenomena using intrinsic time, and we compare our results with an extrinsic time scale. This allows us to highlight the fact that the same phenomenon observed at two different time scales can have a very different behavior. We then analyze the relevance of the use of intrinsic time scale for detecting dynamic communities. Comparing communities obtained according extrinsic and intrinsic scales shows that the intrinsic time scale allows a more significant detection than extrinsic time scale
Hérault, Laurent. "Réseaux de neurones récursifs pour l'optimisation combinatoire : application à la théorie des graphes et à la vision par ordinateur." Grenoble INPG, 1991. http://www.theses.fr/1991INPG0019.
Повний текст джерелаFaucheux, Cyrille. "Segmentation supervisée d'images texturées par régularisation de graphes." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4050/document.
Повний текст джерелаIn this thesis, we improve a recent image segmentation algorithm based on a graph regularization process. The goal of this method is to compute an indicator function that satisfies a regularity and a fidelity criteria. Its particularity is to represent images with similarity graphs. This data structure allows relations to be established between similar pixels, leading to non-local processing of the data. In order to improve this approach, combine it with another non-local one: the texture features. Two solutions are developped, both based on Haralick features. In the first one, we propose a new fidelity term which is based on the work of Chan and Vese and is able to evaluate the homogeneity of texture features. In the second method, we propose to replace the fidelity criteria by the output of a supervised classifier. Trained to recognize several textures, the classifier is able to produce a better modelization of the problem by identifying the most relevant texture features. This method is also extended to multiclass segmentation problems. Both are applied to 2D and 3D textured images
Wauquier, Pauline. "Task driven representation learning." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30005/document.
Повний текст джерелаMachine learning proposes numerous algorithms to solve the different tasks that can be extracted from real world prediction problems. To solve the different concerned tasks, most Machine learning algorithms somehow rely on relationships between instances. Pairwise instances relationships can be obtained by computing a distance between the vectorial representations of the instances. Considering the available vectorial representation of the data, none of the commonly used distances is ensured to be representative of the task that aims at being solved. In this work, we investigate the gain of tuning the vectorial representation of the data to the distance to more optimally solve the task. We more particularly focus on an existing graph-based algorithm for classification task. An algorithm to learn a mapping of the data in a representation space which allows an optimal graph-based classification is first introduced. By projecting the data in a representation space in which the predefined distance is representative of the task, we aim at outperforming the initial vectorial representation of the data when solving the task. A theoretical analysis of the introduced algorithm is performed to define the conditions ensuring an optimal classification. A set of empirical experiments allows us to evaluate the gain of the introduced approach and to temper the theoretical analysis
Pasdeloup, Bastien. "Extending convolutional neural networks to irregular domains through graph inference." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0048/document.
Повний текст джерелаThis manuscript sums up our work on extending convolutional neuralnetworks to irregular domains through graph inference. It consists of three main chapters, each giving the details of a part of a methodology allowing the definition of such networks to process signals evolving on graphs with unknown structures.First, graph inference from data is explored, in order to provide a graph modeling the support of the signals to classify. Second, translation operators that preserve neighborhood properties of the vertices are identified on the inferred graph. Third, these translations are used to shift a convolutional kernel on the graph in order to define a convolutional neural network that is adapted to the input data.We have illustrated our methodology on a dataset of images. While not using any particular knowledge on the signals, we have been able to infer a graph that is close to a grid. Translations on this graph resemble Euclidean translations. Therefore, this has allowed us to define an adapted convolutional neural network that is very close what one would obtain when using the information that signals are images. This network, trained on the initial data, has out performed state of the art methods by more than 13 points, while using a very simple and easily improvable architecture.The method we have introduced is a generalization of convolutional neural networks. As a matter of fact, they can be seen as aparticularization of our approach in the case where the graph is a grid. Our work thus opens the way to numerous perspectives, as it provides an efficient way to build networks that are adapted to the data
Rosar, Kós Lassance Carlos Eduardo. "Graphs for deep learning representations." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0204.
Повний текст джерелаIn recent years, Deep Learning methods have achieved state of the art performance in a vast range of machine learning tasks, including image classification and multilingual automatic text translation. These architectures are trained to solve machine learning tasks in an end-to-end fashion. In order to reach top-tier performance, these architectures often require a very large number of trainable parameters. There are multiple undesirable consequences, and in order to tackle these issues, it is desired to be able to open the black boxes of deep learning architectures. Problematically, doing so is difficult due to the high dimensionality of representations and the stochasticity of the training process. In this thesis, we investigate these architectures by introducing a graph formalism based on the recent advances in Graph Signal Processing (GSP). Namely, we use graphs to represent the latent spaces of deep neural networks. We showcase that this graph formalism allows us to answer various questions including: ensuring generalization abilities, reducing the amount of arbitrary choices in the design of the learning process, improving robustness to small perturbations added to the inputs, and reducing computational complexity
Pineau, Edouard. "Contributions to representation learning of multivariate time series and graphs." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT037.
Повний текст джерелаMachine learning (ML) algorithms are designed to learn models that have the ability to take decisions or make predictions from data, in a large panel of tasks. In general, the learned models are statistical approximations of the true/optimal unknown decision models. The efficiency of a learning algorithm depends on an equilibrium between model richness, complexity of the data distribution and complexity of the task to solve from data. Nevertheless, for computational convenience, the statistical decision models often adopt simplifying assumptions about the data (e.g. linear separability, independence of the observed variables, etc.). However, when data distribution is complex (e.g. high-dimensional with nonlinear interactions between observed variables), the simplifying assumptions can be counterproductive. In this situation, a solution is to feed the model with an alternative representation of the data. The objective of data representation is to separate the relevant information with respect to the task to solve from the noise, in particular if the relevant information is hidden (latent), in order to help the statistical model. Until recently and the rise of modern ML, many standard representations consisted in an expert-based handcrafted preprocessing of data. Recently, a branch of ML called deep learning (DL) completely shifted the paradigm. DL uses neural networks (NNs), a family of powerful parametric functions, as learning data representation pipelines. These recent advances outperformed most of the handcrafted data in many domains.In this thesis, we are interested in learning representations of multivariate time series (MTS) and graphs. MTS and graphs are particular objects that do not directly match standard requirements of ML algorithms. They can have variable size and non-trivial alignment, such that comparing two MTS or two graphs with standard metrics is generally not relevant. Hence, particular representations are required for their analysis using ML approaches. The contributions of this thesis consist of practical and theoretical results presenting new MTS and graphs representation learning frameworks.Two MTS representation learning frameworks are dedicated to the ageing detection of mechanical systems. First, we propose a model-based MTS representation learning framework called Sequence-to-graph (Seq2Graph). Seq2Graph assumes that the data we observe has been generated by a model whose graphical representation is a causality graph. It then represents, using an appropriate neural network, the sample on this graph. From this representation, when it is appropriate, we can find interesting information about the state of the studied mechanical system. Second, we propose a generic trend detection method called Contrastive Trend Estimation (CTE). CTE learns to classify pairs of samples with respect to the monotony of the trend between them. We show that using this method, under few assumptions, we identify the true state underlying the studied mechanical system, up-to monotone scalar transform.Two graph representation learning frameworks are dedicated to the classification of graphs. First, we propose to see graphs as sequences of nodes and create a framework based on recurrent neural networks to represent and classify them. Second, we analyze a simple baseline feature for graph classification: the Laplacian spectrum. We show that this feature matches minimal requirements to classify graphs when all the meaningful information is contained in the structure of the graphs
Kalainathan, Diviyan. "Generative Neural Networks to infer Causal Mechanisms : algorithms and applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS516.
Повний текст джерелаCausal discovery is of utmost importance for agents who must plan, reason and decide based on observations; where mistaking correlation with causation might lead to unwanted consequences. The gold standard to discover causal relations is to perform experiments.However, experiments are in many cases expensive, unethical, or impossible to realize. In these situations, there is a need for observational causal discovery, that is, the estimation of causal relations from observations alone.Causal discovery in the observational data setting traditionally involves making significant assumptions on the data and on the underlying causal model.This thesis aims to alleviate some of the assumptions made on the causal models by exploiting the modularity and expressiveness of neural networks for causal discovery, leveraging both conditional independences and simplicity of the causal mechanisms through two algorithms.Extensive experiments on both simulated and real-world data and a throughout theoretical anaylsis prove the good performance and the soundness of the proposed approaches
Aracena, Julio. "Modèles mathématiques discrets associées à des systèmes biologiques : applications aux réseaux de régulation génétique." Université Joseph Fourier (Grenoble ; 1971-2015), 2001. http://www.theses.fr/2001GRE10215.
Повний текст джерелаAracena, Julio. "Modèles mathématiques discrets associées à des systèmes biologiques : applications aux réseaux de régulation génétique." Université Joseph Fourier (Grenoble), 2001. http://www.theses.fr/2001GRE1A004.
Повний текст джерелаElagouni, Khaoula. "Combining neural-based approaches and linguistic knowledge for text recognition in multimedia documents." Thesis, Rennes, INSA, 2013. http://www.theses.fr/2013ISAR0013/document.
Повний текст джерелаThis thesis focuses on the recognition of textual clues in images and videos. In this context, OCR (optical character recognition) systems, able to recognize caption texts as well as natural scene texts captured anywhere in the environment have been designed. Novel approaches, robust to text variability (differentfonts, colors, sizes, etc.) and acquisition conditions (complex background, non uniform lighting, low resolution, etc.) have been proposed. In particular, two kinds of methods dedicated to text recognition are provided:- A segmentation-based approach that computes nonlinear separations between characters well adapted to the localmorphology of images;- Two segmentation-free approaches that integrate a multi-scale scanning scheme. The first one relies on a graph model, while the second one uses a particular connectionist recurrent model able to handle spatial constraints between characters.In addition to the originalities of each approach, two extra contributions of this work lie in the design of a character recognition method based on a neural classification model and the incorporation of some linguistic knowledge that enables to take into account the lexical context.The proposed OCR systems were tested and evaluated on two datasets: a caption texts video dataset and a natural scene texts dataset (namely the public database ICDAR 2003). Experiments have demonstrated the efficiency of our approaches and have permitted to compare their performances to those of state-of-the-art methods, highlighting their advantages and limits
Chen, Dexiong. "Modélisation de données structurées avec des machines profondes à noyaux et des applications en biologie computationnelle." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM070.
Повний текст джерелаDeveloping efficient algorithms to learn appropriate representations of structured data, including sequences or graphs, is a major and central challenge in machine learning. To this end, deep learning has become popular in structured data modeling. Deep neural networks have drawn particular attention in various scientific fields such as computer vision, natural language understanding or biology. For instance, they provide computational tools for biologists to possibly understand and uncover biological properties or relationships among macromolecules within living organisms. However, most of the success of deep learning methods in these fields essentially relies on the guidance of empirical insights as well as huge amounts of annotated data. Exploiting more data-efficient models is necessary as labeled data is often scarce.Another line of research is kernel methods, which provide a systematic and principled approach for learning non-linear models from data of arbitrary structure. In addition to their simplicity, they exhibit a natural way to control regularization and thus to avoid overfitting.However, the data representations provided by traditional kernel methods are only defined by simply designed hand-crafted features, which makes them perform worse than neural networks when enough labeled data are available. More complex kernels inspired by prior knowledge used in neural networks have thus been developed to build richer representations and thus bridge this gap. Yet, they are less scalable. By contrast, neural networks are able to learn a compact representation for a specific learning task, which allows them to retain the expressivity of the representation while scaling to large sample size.Incorporating complementary views of kernel methods and deep neural networks to build new frameworks is therefore useful to benefit from both worlds.In this thesis, we build a general kernel-based framework for modeling structured data by leveraging prior knowledge from classical kernel methods and deep networks. Our framework provides efficient algorithmic tools for learning representations without annotations as well as for learning more compact representations in a task-driven way. Our framework can be used to efficiently model sequences and graphs with simple interpretation of predictions. It also offers new insights about designing more expressive kernels and neural networks for sequences and graphs
Messé, Arnaud. "Caractérisation de la relation structure-fonction dans le cerveau humain à partir de données d'IRM fonctionnelle et de diffusion : méthodes et applications cognitive et clinique." Phd thesis, Université de Nice Sophia-Antipolis, 2010. http://tel.archives-ouvertes.fr/tel-00845014.
Повний текст джерелаHammadi, Youssef. "Réduction d'un modèle 0D instationnaire et non-linéaire de thermique habitacle pour l’optimisation énergétique des véhicules automobiles." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM027.
Повний текст джерелаThe use of automotive air conditioning leads to a fuel overconsumption. To reduce this overconsumption, we can either work upstream on the technical definitions of the cabin and the HVAC system or optimize control strategies. In both cases, it is essential to build a cabin thermal model that well balances accuracy and complexity. This is the topic of this PhD thesis driven by Renault Group. First, a model reduction methodology is used to build a 0D model starting from a 3D finite element cabin thermal model. This 0D model is based on mass and energy balances on the different cabin walls and air zones. It consists of a nonlinear differential algebraic equations system which can be reinterpreted as a Bond Graph. In addition, the 0D model is based on a weak coupling between the thermal equations and the fluid mechanics ones resulting from CFD calculations (internal airflow and external aerodynamics). Secondly, we apply a machine learning method to the data generated by the 0D model in order to build a reduced 0D model. A design of experiment is considered at this stage. Due to the nonlinearity of the heat exchanges, we have developed an approach which is inspired by the Gappy POD and EIM methods. We use a multiphysics reduced basis that takes several contributions into account (temperatures, enthalpies, heat fluxes and humidities). The resulting reduced model is a hybrid model that couples some of the original physical equations to an artificial neural network. The reduction methodology has been validated on Renault vehicles. The reduced order models have been integrated into a vehicle system-level energetic simulation platform (GREEN) which models different thermics (engine, transmission, cooling system, battery, HVAC, refrigerant circuit, underhood) in order to perform thermal management studies which are of particular importance for electric and hybrid vehicles. The reduced order models have been validated on several scenarios (temperature control for thermal comfort, driving cycles, HVAC coupling) and have achieved CPU gains of up to 99% with average errors of 0.5 °C on temperatures and 0.6% on relative humidities
Osman, Ousama. "Méthodes de diagnostic en ligne, embarqué et distribué dans les réseaux filaires complexes." Thesis, Université Clermont Auvergne (2017-2020), 2020. http://www.theses.fr/2020CLFAC038.
Повний текст джерелаThe research conducted in this thesis focuses on the diagnosis of complex wired networks using distributed reflectometry. It aims to develop new distributed diagnostic techniques for complex networks that allow data fusion as well as communication between reflectometers to detect, locate and characterize electrical faults (soft and hard faults). This collaboration between reflectometers solves the problem of fault location ambiguity and improves the quality of diagnosis. The first contribution is the development of a graph theory-based method for combining data between distributed reflectometers, thus facilitating the location of the fault. Then, the amplitude of the reflected signal is used to identify the type of fault and estimate its impedance. The latter is based on the regeneration of the signal by compensating for the degradation suffered by the diagnosis signal during its propagation through the network. The second contribution enables data fusion between distributed reflectometers in complex networks affected by multiple faults. To achieve this objective, two methods have been proposed and developed: the first is based on genetic algorithms (GA) and the second is based on neural networks (RN). These tools combined with distributed reflectometryallow automatic detection, location, and characterization of several faults in different types and topologies of wired networks. The third contribution proposes the use of information-carrying diagnosis signal to integrate communication between distributed reflectometers. It properly uses the phases of the MCTDR multi-carrier signal to transmit data. This communication ensures the exchange of useful information (such as fault location and amplitude) between reflectometers on the state of the cables, thus enabling data fusion and unambiguous fault location. Interference problems between the reflectometers are also addressed when they simultaneously inject their test signals into the network. These studies illustrate the efficiency and applicability of the proposed methods. They also demonstrate their potential to improve the performance of the current wired diagnosis systems to meet the need and the problem of detecting and locating faults that manufacturers and users face today in electrical systems to improve their operational safety
Qian, Yang. "Conception et Commande d’un Robot d’Assistance à la Personne." Thesis, Ecole centrale de Lille, 2013. http://www.theses.fr/2013ECLI0005/document.
Повний текст джерелаThe purpose of this thesis is to design, model and control of a personal assistant robot used for domestic tasks. In order to make the robot’s design more efficient, a virtual simulation system is built using dynamic simulation software. The kinematic model is set up based on modified D-H principle. The dynamic model is built using the Lagrange theorem and elaborated in Matlab. We also employ an energy-based approach for modeling and its bond graph notation ensures encapsulation of functionality, extendibility and reusability of each element of the model. A hybrid algorithm of combining the Jacobian pseudoinverse algorithm with Rapidly-Exploring Random Tree method is presented for collision-free path planning of a redundant manipulator. An intelligent robust controller based on neural network is introduced for the coordinated control of a mobile manipulator. This method does not require an accurate model of the robot. Unknown dynamic parameters of the mobile platform and the manipulator are identified and compensated in closed-loop control using RBF neural network. A similar control algorithm is presented for coordinated force/motion control of a mobile manipulator suffering both holonomic and nonholonomic constraints. Kinematics and dynamics of a dexterous hand manipulating an object with known shape by rolling contacts are derived. A computed torque control algorithm is presented to ensure firm grip, avoid slippage and well track a given motion imposed to the object. The validation of models and different control laws were made by the co-simulation Matlab / virtual model
Hsieh, Tsung-Ying, and 謝宗穎. "Polyethylenimine-modificated Graphene Carrying DNA with Aid of External Trigger as Targeted Vector in Neuron Gene Therapy." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/k7y767.
Повний текст джерела國立交通大學
材料科學與工程學系所
103
In the past few years, the current therapies for neurodegenerative diseases are not efficient because the morphology and properties of neurons are very different from normal cells. However, recently gene therapy is gradually developed as a new solution that have the potential for to cure neurodegenerative diseases at molecular level. Addition, the combination of targeting therapy and external stimulation will have the opportunity to increase the drug efficacy. Therefore, our research is to develop a gene delivery system and to investigate its behavior and transfection efficiency. In the first part of this study, a neuron-specific gene delivery system is developed by conjugating a neurotensin and poly(ethylenimine)-modificated reduced graphene oxide (rGO) via electrostatic force. This rGO-PEI-NT nanoparticle which stably protects plasmid DNA (pDNA) from digesting performs great targeting ability toward neuron-like cells. According to the literature, polyethyleneimine (PEI) has excellent transfection ability, the endosomal escaping power, and the ability to protect plasmid DNA from digesting; however, cell toxicity of PEI is high. In order to fix this problem, we conjugated neurotensin on PEI. The results show that neurotensin not only reduces the cytotoxicity of nanoparticles, but also increased the targeting ability toward neurons. We can observed that a large number of rGO-PEI-NT effectively accumulate in differentiated PC-12 by photoluminescence (PL) microscopy and confocal laser scanning microscopy (CLSM). From the transfection experiments in vitro and in vivo, the transfection efficiency are exactly improved by neurotensin. In the second part, in addition to the nanoparticles we mention in part one, we also combine it with external NIR laser. The thermal-vibration effect generated by rGO under NIR irradiation not only temporarily increase the permeability of cell membrane but also increase the possibility to escape from digesting in endo/lysosome. In our experiment, there are two steps of NIR laser, the function of first step laser (laser-step-1) is to increase the amount of nanoparticles internalization by temporarily increasing the permeability of the cell membrane. On the other hand, the second step of the laser (laser-step-2) to increase the chances of nanoparticles escaping from endo/lysosome. After the NIR irradiation, it can be found that nanoparticles gradually released from the lysosome into the cytoplasm, confirming the near-infrared laser light did increase the chance to escape from endo/lysosomes. Overall, both in extracellular or intracellular part, near-infrared laser does increase the efficiency of drug delivery and solve one of the factors that affect the transfection efficiency most. Besides, from the results of transfection experiment, we can conclude that second step of laser play a crucial role more than first step of laser, namely, the real key point affect the transfection is not captured by endo/lysosome.
Amini, Ladan. "Développement de Graphe de Connectivité Différentiel pour Caractérisation des Régions Cérébrales Impliquées dans l'Epilepsie." Phd thesis, 2010. http://tel.archives-ouvertes.fr/tel-00559915.
Повний текст джерелаDelalleau, Olivier. "Apprentissage machine efficace : théorie et pratique." Thèse, 2012. http://hdl.handle.net/1866/8669.
Повний текст джерелаDespite constant progress in terms of available computational power, memory and amount of data, machine learning algorithms need to be efficient in how they use them. Although minimizing cost is an obvious major concern, another motivation is to attempt to design algorithms that can learn as efficiently as intelligent species. This thesis tackles the problem of efficient learning through various papers dealing with a wide range of machine learning algorithms: this topic is seen both from the point of view of computational efficiency (processing power and memory required by the algorithms) and of statistical efficiency (n umber of samples necessary to solve a given learning task).The first contribution of this thesis is in shedding light on various statistical inefficiencies in existing algorithms. Indeed, we show that decision trees do not generalize well on tasks with some particular properties (chapter 3), and that a similar flaw affects typical graph-based semi-supervised learning algorithms (chapter 5). This flaw is a form of curse of dimensionality that is specific to each of these algorithms. For a subclass of neural networks, called sum-product networks, we prove that using networks with a single hidden layer can be exponentially less efficient than when using deep networks (chapter 4). Our analyses help better understand some inherent flaws found in these algorithms, and steer research towards approaches that may potentially overcome them. We also exhibit computational inefficiencies in popular graph-based semi-supervised learning algorithms (chapter 5) as well as in the learning of mixtures of Gaussians with missing data (chapter 6). In both cases we propose new algorithms that make it possible to scale to much larger datasets. The last two chapters also deal with computational efficiency, but in different ways. Chapter 7 presents a new view on the contrastive divergence algorithm (which has been used for efficient training of restricted Boltzmann machines). It provides additional insight on the reasons why this algorithm has been so successful. Finally, in chapter 8 we describe an application of machine learning to video games, where computational efficiency is tied to software and hardware engineering constraints which, although often ignored in research papers, are ubiquitous in practice.