Дисертації з теми "Neurosciences mathématiques"
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Tonnelier, Arnaud. "Dynamique non-linéaire et bifurcations en neurosciences mathématiques." Université Joseph Fourier (Grenoble), 2001. http://www.theses.fr/2001GRE10186.
Повний текст джерелаWe study properties of excitable systems coming from mathematical modeling in neurosciences. These models are written using couples nonlinear differential equations for which we look for emergent biophysical mechanisms using mathematical tools coming from bifurcation theory or perturbative methods. Most analytical results are obtained using an idealized nonlinearity with the Heaviside step function. Firstly, we study the piecewise linear FitzHugh-Nagumo model and its generalization to a Linéard system. Specifically, we are interested in transient regime, i. E. The emission of a finite number of action potentials, and asymptotic regime, i. E. The existence of limit cycles. For other models, neural populations model and neural oscillators model, we determine the bifurcation and we study synchronisation phenomena. We finish by studying synaptic propagation in neural network, and saltatory propoagation, along the neuron axon. (. . . )
Touboul, Jonathan. "Modèles nonlinéaires et stochastiques en neuroscience." Palaiseau, Ecole polytechnique, 2008. http://www.theses.fr/2008EPXX0028.
Повний текст джерелаSaïghi, Sylvain. "Circuits et systèmes de modélisation analogique de réseaux de neurones biologiques : application au développement d'outils pour les neurosciences computationnelles." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2004. http://tel.archives-ouvertes.fr/tel-00326005.
Повний текст джерелаMolaee-Ardekani, Behnam. "Modelling electrical activities of the brain and analysis of the EEG in general anesthesia." Rennes 1, 2008. http://www.theses.fr/2008REN1S154.
Повний текст джерелаUn modèle de type populations de neurones dédié à la simulation de l?EEG pour différentes profondeurs d'anesthésie (DOA) est présenté. Ses ingrédients sont issus des travaux de Steyn-Ross & Liley, et son originalité est l?adjonction d?un nouveau mécanisme ionique lent. La fonction sigmoïdale de Wilson-Cowan est redéfinie pour être également une fonction du mécanisme ionique lent introduit. Quand un agent anesthésique est administré, le mécanisme lent impose deux états de fonctionnement pour les cellules neurales. En effet, celles-ci alternent entre deux niveaux d'activité (haut, bas). La fréquence de commutation entre ces états est dans la bande Delta (c'est la raison derrière l'amplitude élevée de l?EEG durant l'anesthésie). Dans la phase de réveil le modèle est à l?état haut, en anesthésie modérée le modèle bascule dans le mode alterné et en anesthésie profonde il reste dans l?état bas. La modulation des ondes Alpha par des activités EEG plus lentes est également étudiée dans diverses DOA. La modulation est mesurée par deux paramètres appelés phase et taux de la modulation (POM, SOM). Ces paramètres sont calculés pour différents sous-bandes de la bande Delta et sont utilisés pour isoler différents mécanismes neurophysiologiques contribuant à la bande Delta, et pour déterminer DOA. Le paramètre SOM indique que la bande Delta comporte trois sous-bandes principales (approximativement [0. 1-0. 5],[0. 5-1. 5],[2-4]Hz). Les variations de POM en lien avec le volume du Désflurane indiquent que ce paramètre peut contribuer à l?évaluation de DOA. Le paramètre POM pour la bande [1. 7-4]Hz permet de distinguer les niveaux d?anesthésie profonde et légère mieux que l'indice BIS
Caianiello, Eduardo. "Le fait génétique des mathématiques et la puissance dynamique du mental humain." Phd thesis, Ecole des Hautes Etudes en Sciences Sociales (EHESS), 2010. http://tel.archives-ouvertes.fr/tel-00589733.
Повний текст джерелаCogliati, Dezza Irene. "“Vanilla, Vanilla .but what about Pistachio?” A Computational Cognitive Clinical Neuroscience Approach to the Exploration-Exploitation Dilemma." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/278730/3/Document1.pdf.
Повний текст джерелаDoctorat en Sciences psychologiques et de l'éducation
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Vigot, Alexis. "Représentation stochastique d'équations aux dérivées partielles d'ordre supérieur à 3 issues des neurosciences." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066484.
Повний текст джерелаThis Thesis consists of two parts. In the mathematical part we study Korteweg--de Vries (KdV) equation and high-order pdes with a probabilistic point of view in order to obtain Feynman-Kac (FK) type formulas. This study was motivated by recent biological models. We prove a FK representation for a larger class of solutions of KdV equation (not only n-solitons), using Fredholm determinants and Laplace transforms of iterated Skorohod integrals. Regarding higher order pdes, iterated processes that consist in the composition of two independent processes, one corresponding to position and the other one to time, are naturally related to their solutions. Indeed, we prove FK formulas for solutions of high order pdes based on functionals of iterated processes even in the non Markovian case, thus extending the existing results. We also propose a scheme for the simulation of iterated diffusions trajectories based on Euler scheme, that converges a.s., uniformly in time, with a rate of convergence of order $1/4$. An estimation of the error is proposed. In the biological part, we have collected several papers in neuroscience and other fields of biology where the previous types of pdes are involved. In particular, we are interested in the simulation of the propagation of the action potential when the capacitance of the cell membrane is not assumed to be constant. These papers have in common the fact that they question the famous Hodgkin Huxley model dating back to the fifties. Indeed this model even if it has been very efficient for the understanding of neuronal signaling does not take into account all the phenomena that occur during the propagation of the action potential
Chateau-Laurent, Hugo. "Modélisation Computationnelle des Interactions Entre Mémoire Épisodique et Contrôle Cognitif." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0019.
Повний текст джерелаEpisodic memory is often illustrated with the madeleine de Proust excerpt as the ability to re-experience a situation from the past following the perception of a stimulus. This simplistic scenario should not lead into thinking that memory works in isolation from other cognitive functions. On the contrary, memory operations treat highly processed information and are themselves modulated by executive functions in order to inform decision making. This complex interplay can give rise to higher-level functions such as the ability to imagine potential future sequences of events by combining contextually relevant memories. How the brain implements this construction system is still largely a mystery. The objective of this thesis is to employ cognitive computational modeling methods to better understand the interactions between episodic memory, which is supported by the hippocampus, and cognitive control, which mainly involves the prefrontal cortex. It provides elements as to how episodic memory can help an agent to act. It is shown that Neural Episodic Control, a fast and powerful method for reinforcement learning, is in fact mathematically close to the traditional Hopfield Network, a model of associative memory that has greatly influenced the understanding of the hippocampus. Neural Episodic Control indeed fits within the Universal Hopfield Network framework, and it is demonstrated that it can be used to store and recall information, and that other kinds of Hopfield networks can be used for reinforcement learning. The question of how executive functions can control episodic memory operations is also tackled. A hippocampus-inspired network is constructed with as little assumption as possible and modulated with contextual information. The evaluation of performance according to the level at which contextual information is sent provides design principles for controlled episodic memory. Finally, a new biologically inspired model of one-shot sequence learning in the hippocampus is proposed. The model performs very well on multiple datasets while reproducing biological observations. It ascribes a new role to the recurrent collaterals of area CA3 and the asymmetric expansion of place fields, that is to disambiguate overlapping sequences by making retrospective splitter cells emerge. Implications for theories of the hippocampus are discussed and novel experimental predictions are derived
Ebadzadeh, Mohamad Mehdi. "Modélisation des voies réflexes et cérébelleuses, permettant le calcul des fonctions inverses : application à la commande d'un actionneur à deux muscles pneumatiques." Paris, ENST, 2004. http://www.theses.fr/2004ENST0046.
Повний текст джерелаAmalric, Marie. "Etude des mécanismes cérébraux d'apprentissage et de traitement des concepts mathématiques de haut niveau." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066143/document.
Повний текст джерелаHow does the human brain conceptualize abstract ideas? In particular, what is the origin of mathematical activity, especially when it is associated with high-level of abstraction? Is mathematical thought independent of language? Cognitive science has now started to investigate this question that has been of great interest to philosophers, mathematicians and educators for a long time. While studies have so far focused on basic arithmetic processing, my PhD thesis aims at further investigating the cerebral processes involved in the manipulation and learning of more advanced mathematical ideas. I have shown that (1) advanced mathematical reflection on concepts mastered for many years does not recruit the brain circuits for language; (2) mathematical activity systematically involves number- and space-related brain regions, regardless of mathematical domain, problem difficulty, and participants' visual experience; (3) non-verbal acquisition of geometrical rules relies on a language of thought that is independent of natural spoken language. Finally, altogether these results raise new questions and pave the way to further investigations in neuroscience: - is the human ability for language also irrelevant to advanced mathematical acquisition in schools where knowledge is taught verbally? - What is the operational definition of the fields of “mathematics” and “language” at the brain level?
Naze, Sebastien. "Multiscale Computational Modeling of Epileptic Seizures : from macro to microscopic dynamics." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4023/document.
Повний текст джерелаThis thesis consists in the development of a network model of spiking neurons and the systematic investigation of conditions under which the network displays the emergent dynamic behaviors known from the Epileptor, a well-investigated abstract model of epileptic neural activity. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings between neurons play an essential role in seizure genesis. We demonstrate that spike-waves discharges, including interictal spikes, can be generated primarily by inhibitory neurons only, whereas excitatory neurons are responsible for the fast discharges during the wave part. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes locally, and excitatory synaptic and extracellular couplings participate in seizure spread globally across brain regions
Albert, Mélisande. "Tests d’indépendance par bootstrap et permutation : étude asymptotique et non-asymptotique. Application en neurosciences." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4079/document.
Повний текст джерелаOn the one hand, we construct such tests based on bootstrap and permutation approaches. Their asymptotic performance are studied in a point process framework through the analysis of the asymptotic behavior of the conditional distributions of both bootstrapped and permuted test statistics, under the null hypothesis as well as under any alternative. A simulation study is performed verifying the usability of these tests in practice, and comparing them to existing classical methods in Neuroscience. We then focus on the permutation tests, well known for their non-asymptotic level properties. Their p-values, based on the delayed coincidence count, are implemented in a multiple testing procedure, called Permutation Unitary Events method, to detect the synchronization occurrences between two neurons. The practical validity of the method is verified on a simulation study before being applied on real data. On the other hand, the non-asymptotic performances of the permutation tests are studied in terms of uniform separation rates. A new aggregated procedure based on a wavelet thresholding method is developed in the density framework. Based on Talagrand's fundamental inequalities, we provide a new Bernstein-type concentration inequality for randomly permuted sums. In particular, it allows us to upper bound the uniform separation rate of the aggregated procedure over weak Besov spaces and deduce that this procedure seems to be optimal and adaptive in the minimax sens
Bedez, Mathieu. "Modélisation multi-échelles et calculs parallèles appliqués à la simulation de l'activité neuronale." Thesis, Mulhouse, 2015. http://www.theses.fr/2015MULH9738/document.
Повний текст джерелаComputational Neuroscience helped develop mathematical and computational tools for the creation, then simulation models representing the behavior of certain components of our brain at the cellular level. These are helpful in understanding the physical and biochemical interactions between different neurons, instead of a faithful reproduction of various cognitive functions such as in the work on artificial intelligence. The construction of models describing the brain as a whole, using a homogenization microscopic data is newer, because it is necessary to take into account the geometric complexity of the various structures comprising the brain. There is therefore a long process of rebuilding to be done to achieve the simulations. From a mathematical point of view, the various models are described using ordinary differential equations, and partial differential equations. The major problem of these simulations is that the resolution time can become very important when important details on the solutions are required on time scales but also spatial. The purpose of this study is to investigate the various models describing the electrical activity of the brain, using innovative techniques of parallelization of computations, thereby saving time while obtaining highly accurate results. Four major themes will address this issue: description of the models, explaining parallelization tools, applications on both macroscopic models
Azevedo, Carvalho Nathalie. "Un modèle informatique biologiquement réaliste des oscillations neuronales pathologiques observées dans la maladie de Parkinson." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0077.
Повний текст джерелаMy thesis is based on three axes: to develop a biophysical model, to propose a simulation tool, and exploit the model in simulation.We develop a biophysical model of the neuronal structure involved in Parkinson's disease, the basal ganglia. We simulate physiologically realistic neurons using the Hodgkin-Huxley formalism to incorporate specific ion channels present in different populations of GB neurons, including arkipallidal and proptotypic GPe, as well as dopaminergic D1 and D2 neurons in the striatum, whose cellular properties and connectivity appear to be prominent factors in the oscillatory behavior of the network. Our model is validated by experimental data in healthy conditions of the rat, collected by our biologist collaborators.We propose a simulation tool for impulse neural networks allowing realistic simulations on a large scale, > 1 million neurons, on a parallel machine i.e. Grid'5000, Explor, etc... SiReNe is a neural network simulator developed in the C language. This software is based on a hybrid simulation approach. It combines a numerical integration, Runge-Kutta 2, of the neuronal dynamics and an event-driven generation of the network connectivity during action potentials emissions. This approach, developed during the thesis, allows the simulation of large and very detailedneural networks of the Hodgkin-Huxley type.In the future, our model could be used in simulation to test some hypotheses on the pathological synchronization observed in Parkinson's disease. Like the role of GABAergic synaptic connections and the intrinsic neuronal properties of SK channels which control the precision of neuronal discharge. The simulation of large-scale models could be used to limit pathological synchronization and motor disorders through new neurostimulation methods, such as deep brain stimulation
Bouchacourt, Flora. "Hebbian mechanisms and temporal contiguity for unsupervised task-set learning." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066379/document.
Повний текст джерелаDepending on environmental demands, humans performing in a given task are able to exploit multiple concurrent strategies, for which the mental representations are called task-sets. We examine a candidate model for a specific human experiment, where several stimulus-response mappings, or task-sets, need to be learned and monitored. The model is composed of two interacting networks of mixed-selective neural populations. The decision network learns stimulus-response associations, but cannot learn more than one task-set. Its activity drives synaptic plasticity in a second network that learns event statistics on a longer timescale. When patterns in stimulus-response associations are detected, an inference bias to the decision network guides successive behavior. We show that a simple unsupervised Hebbian mechanism in the second network is sufficient to learn an implementation of task-sets. Their retrieval in the decision network improves performance. The model predicts abrupt changes in behavior depending on the precise statistics of previous responses, corresponding to positive (task-set retrieval) or negative effects on performance. The predictions are borne out by the data, and enable to identify subjects who have learned the task structure. The inference signal correlates with BOLD activity in the fronto-parietal network. Within this network, dorsomedial and dorsolateral prefrontal nodes are preferentially recruited when task-sets are recurrent: activity in these regions may provide a bias to decision circuits when a task-set is retrieved. These results show that Hebbian mechanisms and temporal contiguity may parsimoniously explain the learning of rule-guided behavior
Ambard, Maxime. "Influence de l'inhibition synaptique sur le codage de l'information par les cellules mitrales du bulbe olfactif." Phd thesis, Université Henri Poincaré - Nancy I, 2009. http://tel.archives-ouvertes.fr/tel-00401813.
Повний текст джерелаDans un premier temps, l'analyse de données expérimentales recueillies en condition in vitro dans des tranches de bulbe olfactif de rats révèle le caractère phasé des potentiels d'action des cellules mitrales relativement aux oscillations du potentiel de champ local. Ce phasage est largement atténué lorsque l'on bloque pharmacologiquement l'inhibition provenant des granules, mettant ainsi en évidence le rôle primordial de l'inhibition synaptique. Afin d'extraire le décours temporel de la conductance synaptique inhibitrice, nous proposons une nouvelle méthode basée sur l'ajustement d'un modèle de neurone associé à l'injection de bloqueurs synaptiques. Grâce à celle-ci, nous observons que les fluctuations de la conductance synaptique inhibitrice sont corrélées à celles mesurées sur le potentiel de champ local. Une relation entre l'inhibition reçue et la phase des potentiels d'action est également dévoilée. Un neurone aura plus de chance d'émettre en phase s'il reçoit un nombre important d'événements synaptiques inhibiteurs et si ces événements sont eux-même phasés.
Dans un deuxième temps, les résultats de cette analyse sont rassemblés au sein d'un modèle informatique de bulbe olfactif afin d'explorer les capacités de codage de l'interaction mitrale-granule. Après avoir montré que le transfert d'information des cellules mitrales semble plus résider dans leurs instants précis d'émission de potentiels d'action au cours des oscillations que dans leurs fréquences de décharges, nous étudions analytiquement l'influence du nombre d'événements synaptiques inhibiteurs reçus et de leur dispersion temporelle sur la précision de l'activité des cellules mitrales. Notre étude conclut que la robustesse du code produit par les cellules mitrales lors des oscillations du réseau est conditionnée par une forte interaction synaptique entre les cellules mitrales et les cellules granulaires. En dernier lieu, nous appliquons notre modèle de bulbe olfactif pour reconnaître des odeurs à l'aide d'une matrice de capteurs de gaz artificiels.
Huth, Jacob. "Modelling Aging in the Visual System & The Convis Python Toolbox." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS140.
Повний текст джерелаIn this thesis we investigate aging processes in the visual system from a computational modelling perspective. We give a review about neural aging phenomena, basic aging changes and possible mechanisms that can connect causes and effects. The hypotheses we formulate from this review are: the input noise hypothesis, the plasticity hypothesis, the white matter hypothesis and the inhibition hypothesis. Since the input noise hypothesis has the possibility to explain a number of aging phenomena from a very simple premise, we focus mainly on this theory. Since the size and organization of receptive fields is important for perception and is changing in high age, we developed a theory about the interaction of noise and receptive field structure. We then propose spike-time dependent plasticity (STDP) as a possible mechanism that could change receptive field size in response to input noise. In two separate chapters we investigate the approaches to model neural data and psychophysical data respectively. In this process we examine a contrast gain control mechanism and a simplified cortical model respectively. Finally, we present convis, a Python toolbox for creating convolutional vision models,which was developed during the studies for this thesis. convis can implement the most important models used currently to model responses of retinal ganglion cells and cells in the lower visual cortices (V1 and V2)
Mancini, Simona. "Modèles cinétiques. Applications en volcanologie et neurosciences." Habilitation à diriger des recherches, Université d'Orléans, 2012. http://tel.archives-ouvertes.fr/tel-00751434.
Повний текст джерелаMaama, Mohamed. "Dynamiques de réseaux complexes, modélisation et simulations : application au cortex visuel Emergent Properties in a V1 Network of Hodgkin-Huxley Neurons." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMLH07.
Повний текст джерелаThe aim of this work is to analyze theoretically and numerically the dynamics of a network of excitatory and inhibitory neurons of ordinary differential equations (ODE) of Hodgkin-Huxley type (HH) inspired by the primary visual cortex V1. The model emphasizes an approach combining a driven stochastic drive for each neuron and recurrent inputs resulting from the network activity. After a review of the dynamics of a single HH equation, for both deterministic and stochastic driven case, we proceed to the analysis of the network. Our numerical analysis highlights emergent properties such as partial synchronization and synchronization, waves of excitability, and oscillations in the gamma-band frequency
Labache, Loïc. "Création d'Atlas des Réseaux Cérébraux Sous-tendant les Fonctions Cognitives Latéralisées : Application à l'Étude de la Variabilité Inter-individuelle du Langage." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0155.
Повний текст джерелаMy thesis work is part of a multi-modal and multi-scale integration approach which has led to the emergence of cognitive and population neuroimaging. More specifically, fMRI provides two types of three-dimensional functional brain maps: activation maps allowing for visualizing brain regions directly involved in a cognitive process, and intrinsic connectivity maps measuring the synchronization between spatially distant but functionally connected regions. I have applied new statistical methodologies to these two types of maps, allowing me to deal with both the individual and the spatial dimensions. In the first part, I designed atlases of brain regions dedicated to specific cognitive functions, based on their hemispheric lateralization and targeting a population selected for its low variability. I present here the first two language atlases. Indeed, although there are many approaches to map language areas in patients, there was no atlas of networks supporting language functions in healthy individuals so far. I first identified left activated and left asymmetrical regions, both during sentence production, listening and reading, in 137 healthy right-handed individuals. Analysis of the intrinsic connectivity between the 32 identified regions reveals that they are part of 3 distinct functional networks, which constitute the SENSAAS (SENtence Supramodal Areas AtlaS) brain atlas. Among these networks, one with 18 regions contains the essential language areas (SENT_CORE), i.e. the brain areas whose lesion leads to an impairment in the integration of the meaning of speech. Specifically, SENT_CORE contains 3 hubs supporting the information integration and dissemination, localized in the Broca and Wernicke area. I then applied this methodology to the elaboration of an atlas of word processing networks. I identified 21 brain regions organized into 2 distinct networks, one of which is a phonological network including the audio-motor loop. For the first time, a strong intrinsic connectivity between the left audio-motor loop and the prosodic processing, located in the upper temporal sulcus of the right hemisphere, is evidenced. Finally, I developed a new method for studying the variability of three-dimensional data. This new method includes two different mathematical tools based on hierarchical agglomerative clustering algorithms. The first one makes it possible to identify variables leading to partition instability, the second one allows for extracting stable sub-populations from a starting population. The applications of all of this work are numerous: for example, I used the SENT_CORE network to study the inter-individual variability of hemispheric lateralization of the sentence supramodal areas. I have thus identified two groups of typical asymmetric left language individuals, with high left intra-hemispheric intrinsic connectivity and low inter-hemispheric connectivity, and a group of atypical individuals: rightward asymmetrical for language, with high intrinsic connectivity of language networks in both hemispheres and high inter-hemispheric connectivity. SENSAAS has also been used to study the genetic support of language atypicality, as well as for the topological characterization of the memory and language networks of individuals with mesial temporal lobe epilepsy. The new method for assessing inter-individual variability was used to evaluate the stability of the intrinsic networks of a new functional atlas adapted for late adulthood
Ambrogi, Elena. "PDEs for neural networks with internal states." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS122.pdf.
Повний текст джерелаIn the context of mathematical neuroscience, the Integrate and Fire model undoubtedly enjoys great fame and a vast literature. Yet, its peculiar mathematical structure, with non-local terms, jumps or partial diffusion mechanisms, combined with the possible co-presence of different time scales, make the study of this equation challenging and always open-ended. The classical model consists of an equation that describes the dynamics of a network of neurons based on the membrane potential of the cells. A network can be interconnected with excitatory or inhibitory linkages or disconnected, in which case the equation will be linear. Our interest is in the asymptotic behaviour of such networks in the linear case, wheremathematical tools such as the entropy and integral method and Harris theory have been useful in proving the convergence to the steady state. In the first extension of the classical Integrate and Fire model we propose, we replace the pointwise boundary condition with a non-local term, introducing a randomness parameter. For this new system, we prove long-time convergence via Harris theory and relative entropy with Poincaré inequality independent of the random parameter. Furthermore, we study the asymptotic convergence of the solutions of this model to those of the classical one. In the second extension, we dealwith the incorporation of a variable for the adaptation current. First, we study the dynamics of this last variable alone, analysing the regularity of the stationary solution in dependence on the parameters and the asymptotic behaviour by means of the different methods of relative entropy with compactness argument and integral method. We then investigate the asymptotic behaviour of the two-dimensional model through numerical simulations and we make comparison with a similar Fokker-Planck equation with partial diffusion and nonlinearity. A number of numerical simulations accompany the study of each analysed model, allowing its theoretical results to be supported or anticipated
Nel contesto delle neuroscienze matematiche, il modello di Integrate and Fire gode indubbiamente di grande fama e di una vasta letteratura. Eppure, la sua peculiare struttura matematica, con termini non-locali, meccanismi di salto o di diffusione parziale, unita all’eventuale compresenza di differenti scale temporali, rendono lo studio di questa equazione stimolante e sempre aperto. Il modello classico consiste in un’equazione che descrive la dinamica di una rete di neuroni in funzione del potenziale di membrana delle cellule. Una rete può essere interconnessa con legami eccitatori o inibitori o disconnessa, nel qual caso l’equazione sarà lineare. Noi siamo interessati al comportamento asintotico di tali reti nel caso lineare, dove strumenti matematici come l’entropia relativa, il metodo integrale e la teoria di Harris si sono rivelati utili per dimostrare la convergenza verso lo stato stazionario. Nella prima estensione del modello classico di Integrate and Fire che proponiamo, sostituiamo la condizione al bordo puntuale con un termine non locale, inserendo un parametro di casualità. Per questo nuovo sistema, dimostriamo la convergenza allo stato stazionario tramite la teoria di Harris e dell’entropia relativa con disuguaglianza di Poincaré indipendente dal parametro casuale. Inoltre, studiamo la convergenza asintotica delle soluzioni di questo modello a quelle del classico. Nella seconda estensione ci occupiamo di incorporare una variabile per la corrente di adattazione. In primo luogo, studiamo la dinamica di quest’ultima variabile sola, analizzando la regolarità della soluzione stazionaria in dipendenza dai parametri e studiando il comportamento asintotico tramite i differenti metodi dell’entropia relativa con argomento di compattezza e metodo integrale. Indaghiamo poi la dinamica del modello bidimensionale tramite delle simulazioni numeriche e lo confrontiamo con un’equazione di Fokker-Planck similare con diffusione parziale e nonlinearità. Alcune simulazioni numeriche accompagnano lo studio di ogni modello analizzato, permettendo così di supportarne o anticiparne i risultati teorici
Topalidou, Meropi. "Neuroscience of decision making : from goal-directed actions to habits." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0174/document.
Повний текст джерелаAction-outcome and stimulus-response processes are two important components of behavior. The former evaluates the benefit of an action in order to choose the best action among those available (action selection) while the latter is responsible for automatic behavior, eliciting a response as soon as a known stimulus is present. Such habits are generally associated (and mostly opposed) to goal-directed actions that require a deliberative process to evaluate the best option to take in order to reach a given goal. Using a computational model, we investigated the classic hypothesis of habits formation and expression in the basal ganglia and proposed a new hypothesis concerning the respective role for both the basal ganglia and the cortex. Inspired by previous theoretical and experimental works (Leblois et al., 2006; Guthrie et al., 2013), we designed a computational model of the basal ganglia-thalamus-cortex that uses segregated loops (motor, cognitive and associative) and makes the hypothesis that basal ganglia are only necessary for the acquisition of habits while the expression of such habits can be mediated through the cortex. Furthermore, this model predicts the existence of covert learning within the basal ganglia ganglia when their output is inhibited. Using a two-armed bandit task, this hypothesis has been experimentally tested and confirmed in monkey. Finally, this works suggest to revise the classical idea that automatism is a subcortical feature
Kuchenbuch, Mathieu. "Modélisation computationelle de l'épilepsie avec crises focales migrantes du nourrisson." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1B062.
Повний текст джерелаEpilepsy in infancy with migrating focal seizures is characterized by focal seizures beginning before 6 months that intensify to a stormy phase where so-called migrating focal seizures appear. The gain-of-function mutations of the KCNT1 gene are the main causes of this epilepsy. We focused on a cohort of patients with a KCNT1 mutations and this epilepsy to better understand this syndrome in order to model it. First, we specified the clinic for these patients, including long-term poor outcomes, high mortality, microcephaly and the presence of extra-neurological symptoms. Then, we determined, through the study of ictal EEGs, that migrating seizures were not chaotic but rather corresponded to a type of propagation and we have identified specific markers of this epilepsy. Then, we showed that the majority of KCNT1 mutations appeared to cluster in "hot spots" and that there was no strict genotypephenotype correlation. Finally, we modelled this epilepsy at microscopic and mesoscopic levels. Preliminary results showed a decrease in excitation, a fall in inhibition and involvement of depolarizing GABA. We then discuss the different aspects of our work in the light of the literature and describe the perspectives opened by this thesis from a fundamental, clinical and physiological point of views
Phan, Van Long Em. "Analyse asymptotique de réseaux complexes de systèmes de réaction-diffusion." Thesis, Le Havre, 2015. http://www.theses.fr/2015LEHA0012/document.
Повний текст джерелаThe neuron, a fundamental unit in the nervous system, is a point of interest in many scientific disciplines. Thus, there are some mathematical models that describe their behavior by ODE or PDE systems. Many of these models can then be coupled in order to study the behavior of networks, complex systems in which the properties emerge. Firstly, this work presents the main mechanisms governing the neuron behaviour in order to understand the different models. Several models are then presented, including the FitzHugh-Nagumo one, which has a interesting dynamic. The theoretical and numerical study of the asymptotic and transitory dynamics of the aforementioned model is then proposed in the second part of this thesis. From this study, the interaction networks of ODE are built by coupling previously dynamic systems. The study of identical synchronization phenomenon in these networks shows the existence of emergent properties that can be characterized by power laws. In the third part, we focus on the study of the PDE system of FHN. As the previous part, the interaction networks of PDE are studied. We have in this section a theoretical and numerical study. In the theoretical part, we show the existence of the global attractor on the space L2(Ω)nd and give the sufficient conditions for identical synchronization. In the numerical part, we illustrate the synchronization phenomenon, also the general laws of emergence such as the power laws or the patterns formation. The diffusion effect on the synchronization is studied
Tiganj, Zoran. "On the pertinence of a numerical transmission model for neural information." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00699623.
Повний текст джерелаVitay, Julien. "Emergence de fonctions sensorimotrices sur un substrat neuronal numérique distribué." Phd thesis, Université Henri Poincaré - Nancy I, 2006. http://tel.archives-ouvertes.fr/tel-00096818.
Повний текст джерелаcomputationnelles dont le but est de modéliser des fonctions
cognitives complexes par le biais de simulations
informatiques et numériques en s'inspirant du fonctionnement
cérébral. Contrairement à une approche descendante nécessitant de
connaître une expression analytique de la fonction à simuler,
l'approche ascendante retenue permet d'observer
l'émergence d'une fonction grâce à l'interaction de populations de
neurones artificiels sans qu'elle soit connue à l'avance. Dans un
premier temps, nous présentons un modèle de réseau de neurones
particulier, les champs neuronaux, dont les propriétés
dynamiques de résistance au bruit et de continuité spatio-temporelle permettent cette émergence. Afin de guider l'émergence de transformations sensorimotrices sur ce substrat, nous présentons ensuite l'architecture des
systèmes visuel et moteur pour mettre en évidence le rôle central de l'attention visuelle dans la réalisation de ces fonctions
par le cerveau. Nous proposons ensuite un schéma
fonctionnel des transformations sensorimotrices dans lequel la
préparation d'une saccade oculaire guide l'attention vers une rÈgion
de l'espace visuel et permet la programmation du mouvement. Nous décrivons enfin un modèle computationnel de déplacement du point d'attention qui, en utilisant une mémoire de travail spatiale
dynamique, permet la recherche séquentielle d'une cible dans une scène visuelle grâce au phénomène d'inhibition de retour. Les performances de ce modèle (résistance au bruit, au mouvement des objets et à l'exécution de saccades) sont analysées en simulation et sur une plate-forme robotique.
Dragoni, Laurent. "Tri de potentiels d'action sur des données neurophysiologiques massives : stratégie d’ensemble actif par fenêtre glissante pour l’estimation de modèles convolutionnels en grande dimension." Thesis, Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ4016.
Повний текст джерелаIn the nervous system, cells called neurons are specialized in the communication of information. Through the generation and propagation of electrical currents named action potentials, neurons are able to transmit information in the body. Given the importance of the neurons, in order to better understand the functioning of the nervous system, a wide range of methods have been proposed for studying those cells. In this thesis, we focus on the analysis of signals which have been recorded by electrodes, and more specifically, tetrodes and multi-electrode arrays (MEA). Since those devices usually record the activity of a set of neurons, the recorded signals are often a mixture of the activity of several neurons. In order to gain more knowledge from this type of data, a crucial pre-processing step called spike sorting is required to separate the activity of each neuron. Nowadays, the general procedure for spike sorting consists in a three steps procedure: thresholding, feature extraction and clustering. Unfortunately this methodology requires a large number of manual operations. Moreover, it becomes even more difficult when treating massive volumes of data, especially MEA recordings which also tend to feature more neuronal synchronizations. In this thesis, we present a spike sorting strategy allowing the analysis of large volumes of data and which requires few manual operations. This strategy makes use of a convolutional model which aims at breaking down the recorded signals as temporal convolutions between two factors: neuron activations and action potential shapes. The estimation of these two factors is usually treated through alternative optimization. Being the most difficult task, we only focus here on the estimation of the activations, assuming that the action potential shapes are known. Estimating the activations is traditionally referred to convolutional sparse coding. The well-known Lasso estimator features interesting mathematical properties for the resolution of such problem. However its computation remains challenging on high dimensional problems. We propose an algorithm based of the working set strategy in order to compute efficiently the Lasso. This algorithm takes advantage of the particular structure of the problem, derived from biological properties, by using temporal sliding windows, allowing it to scale in high dimension. Furthermore, we adapt theoretical results about the Lasso to show that, under reasonable assumptions, our estimator recovers the support of the true activation vector with high probability. We also propose models for both the spatial distribution and activation times of the neurons which allow us to quantify the size of our problem and deduce the theoretical complexity of our algorithm. In particular, we obtain a quasi-linear complexity with respect to the size of the recorded signal. Finally we present numerical results illustrating both the theoretical results and the performances of our approach
Sarmis, Merdan. "Etude de l'activité neuronale : optimisation du temps de simulation et stabilité des modèles." Thesis, Mulhouse, 2013. http://www.theses.fr/2013MULH3848/document.
Повний текст джерелаComputational Neuroscience consists in studying the nervous system through modeling and simulation. It is to characterize the laws of biology by using mathematical models integrating all known experimental data. From a practical point of view, the more realistic the model, the largest the required computational resources. The issue of complexity and accuracy is a well known problem in the modeling and identification of models. The research conducted in this thesis aims at improving the simulation of mathematical models representing the physical and chemical behavior of synaptic receptors. Models of synaptic receptors are described by ordinary differential equations (ODE), and are resolved with numerical procedures. In order to optimize the performance of the simulations, I have implemented various ODE numerical resolution methods. To facilitate the selection of the best solver, a method, requiring a minimum amount of information, has been proposed. This method allows choosing the best solver in order to optimize the simulation. The method demonstrates that the dynamic of a model has greater influence on the solver performances than the kinetic scheme of the model. In addition, to characterize pathogenic behavior, a parameter optimization is performed. However, some parameter values lead to unstable models. A stability study allowed for determining the stability of the models with parameters provided by the literature, but also to trace the stability constraints depending to these parameters. Compliance with these constraints ensures the stability of the models studied during the optimization phase, and therefore the success of the procedure to study pathogen models
Cherdo, Yann. "Détection d'anomalie non supervisée sur les séries temporelle à faible coût énergétique utilisant les SNNs." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4018.
Повний текст джерелаIn the context of the predictive maintenance of the car manufacturer Renault, this thesis aims at providing low-power solutions for unsupervised anomaly detection on time-series. With the recent evolution of cars, more and more data are produced and need to be processed by machine learning algorithms. This processing can be performed in the cloud or directly at the edge inside the car. In such a case, network bandwidth, cloud services costs, data privacy management and data loss can be saved. Embedding a machine learning model inside a car is challenging as it requires frugal models due to memory and processing constraints. To this aim, we study the usage of spiking neural networks (SNNs) for anomaly detection, prediction and classification on time-series. SNNs models' performance and energy costs are evaluated in an edge scenario using generic hardware models that consider all calculation and memory costs. To leverage as much as possible the sparsity of SNNs, we propose a model with trainable sparse connections that consumes half the energy compared to its non-sparse version. This model is evaluated on anomaly detection public benchmarks, a real use-case of anomaly detection from Renault Alpine cars, weather forecasts and the google speech command dataset. We also compare its performance with other existing SNN and non-spiking models. We conclude that, for some use-cases, spiking models can provide state-of-the-art performance while consuming 2 to 8 times less energy. Yet, further studies should be undertaken to evaluate these models once embedded in a car. Inspired by neuroscience, we argue that other bio-inspired properties such as attention, sparsity, hierarchy or neural assemblies dynamics could be exploited to even get better energy efficiency and performance with spiking models. Finally, we end this thesis with an essay dealing with cognitive neuroscience, philosophy and artificial intelligence. Diving into conceptual difficulties linked to consciousness and considering the deterministic mechanisms of memory, we argue that consciousness and the self could be constitutively independent from memory. The aim of this essay is to question the nature of humans by contrast with the ones of machines and AI
Vallée, Alexandre. "Molecular thermodynamic aspects of dissipative structures in oncology, inflammatory and degenerative processes of Central Nervous System diseases." Thesis, Poitiers, 2017. http://www.theses.fr/2017POIT1409.
Повний текст джерелаEnergy metabolism is the primary determinant of cellular viability. Diseases are the sites of numerous metabolic and energetic production abnormalities. Indeed, the altered cells are derived from exergonic processes and emit heat that flows to the surrounding environment. Many irreversible processes can occur through changing the rate of entropy production. This rate represents a thermodynamic quantity that measures these irreversible processes. Entropy rate is increased by several metabolic and thermodynamics abnormalities in brain tumors, inflammatory processes and neurodegenerative diseases. The research works of this thesis have demonstrated and highlighted the existence of a crosstalk between canonical WNT/beta-catenin pathway and PPAR gamma which plays a major role in the reprogramming of cellular energy metabolism between oxidative phosphorylation, aerobic glycolysis and anaerobic glycolysis, of which the equilibrium point of crosstalk between these molecular pathways varies according to tumor, inflammatory and neurodegenerative diseases. These diseases are dissipative structures, that exchange energy or matter with their environment. They are open systems, far-from the thermodynamic equilibrium that operate under non-linear regime evolving to non-stationary states. Far-from-equilibrium thermodynamics are notions driven by circadian rhythms. Indeed, circadian rhythms directly participate in regulating the crosstalk of the studied molecular pathways. This crosstalk represents an innovative therapeutic target, and molecular data usable for molecular imaging in both positive and differential diagnosis of these diseases
Quininao, Cristobal. "Mathematical modeling in neuroscience : collective behavior of neuronal networks & the role of local homeoproteins diffusion in morphogenesis." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066152/document.
Повний текст джерелаThis work is devoted to the study of mathematical questions arising from the modeling of biological systems combining analytic and probabilistic tools. In the first part, we are interested in the derivation of the mean-field equations related to some neuronal networks, and in the study of the convergence to the equilibria of the solutions to the limit equations. In Chapter 2, we use the coupling method to prove the chaos propagation for a neuronal network with delays and random architecture. In Chapter 3, we consider a kinetic FitzHugh-Nagumo equation. We analyze the existence of solutions and prove the nonlinear exponential convergence in the weak connectivity regime. In the second part, we study the role of homeoproteins (HPs) on the robustness of boundaries of functional areas. In Chapter 4, we propose a general model for neuronal development. We prove that in the absence of diffusion, the HPs are expressed on irregular areas. But in presence of diffusion, even arbitrarily small, well defined boundaries emerge. In Chapter 5, we consider the general model in the one dimensional case and prove the existence of monotonic stationary solutions defining a unique intersection point for any arbitrarily small diffusion coefficient. Finally, in the third part, we study a subcritical Keller-Segel equation. We show the chaos propagation without any restriction on the force kernel. Eventually, we demonstrate that the propagation of chaos holds in the entropic sense
Angelhuber, Martin. "The neural circuitry of fear conditioning : a theoretical account." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAJ082/document.
Повний текст джерелаFear conditioning is a successful paradigm for studying neural substrates of emotional learning. In this thesis, two computational models of the underlying neural circuitry are presented. First, the effects of changes in neuronal membrane conductance on input processing are analyzed in a biologically realistic model. We show that changes in tonic inhibitory conductance increase the responsiveness of the network to inputs. Then, the model is analyzed from a functional perspective and predictions that follow from this proposition are discussed. Next, a systems level model is presented based on a recent high-level approach to conditioning. It is proposed that the interaction between fear and extinction neurons in the basal amygdala is a neural substrate of the switching between latent states, allowing the animal to infer causal structure. Important behavioral and physiological results are reproduced and predictions and questions that follow from the main hypothesis are considered
Seriès, Peggy. "Étude théorique des modulations centre/pourtour des propriétés des champs récepteurs du cortex visuel primaire : circuits, dynamiques et corrélats perceptifs." Paris 6, 2002. http://www.theses.fr/2002PA066333.
Повний текст джерелаThe response of primary visual cortex (V1) neurons to a stimulus presented within the receptive field can be modulated by the stimulation of the surround of the receptive field. The origin and functional role of these " center/surround " modulations is yet poorly understood. Using computational methods in interaction with electrophysiological and psychophysical approaches, we try to answer 2 questions : What are the circuits responsible for the diversity of these phenomena ? We provide theoretical tools to evaluate current models, reconcile them in a common formalism and understand how the spatial characteristics of center/surround modulations can result from the known properties of V1 ; What are the consequences of the dynamics of these effects on cortical responses and visual perception ? Our results suggest that V1 responses and the perception of visual objects should depend not only on the spatial context, but also on the temporal context in which these objects are embedded. We discuss the functional implications of this mechanism for the analysis of static and moving objects
Chleq, Nicolas. "Contribution à l'étude du raisonnement temporel : résolution avec contraintes et application à l'abduction en raisonnement temporel." Phd thesis, Ecole Nationale des Ponts et Chaussées, 1995. http://tel.archives-ouvertes.fr/tel-00529412.
Повний текст джерелаChevalier, Jérôme-Alexis. "Statistical control of sparse models in high dimension." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG051.
Повний текст джерелаIn this thesis, we focus on the multivariate inference problem in the context of high-dimensional structured data. More precisely, given a set of explanatory variables (features) and a target, we aim at recovering the features that are predictive conditionally to others, i.e., recovering the support of a linear predictive model. We concentrate on methods that come with statistical guarantees since we want to have a control on the occurrence of false discoveries. This is relevant to inference problems on high-resolution images, where one aims at pixel- or voxel-level analysis, e.g., in neuroimaging, astronomy, but also in other settings where features have a spatial structure, e.g., in genomics. In such settings, existing procedures are not helpful for support recovery since they lack power and are generally not tractable. The problem is then hard both from the statistical modeling point of view, and from a computation perspective. In these settings, feature values typically reflect the underlying spatial structure, which can thus be leveraged for inference. For example, in neuroimaging, a brain image has a 3D representation and a given voxel is highly correlated with its neighbors. We notably propose the ensemble of clustered desparsified Lasso (ecd-Lasso) estimator that combines three steps: i) a spatially constrained clustering procedure that reduces the problem dimension while taking into account data structure, ii) the desparsified Lasso (d-Lasso) statistical inference procedure that is tractable on reduced versions of the original problem, and iii) an ensembling method that aggregates the solutions of different compressed versions of the problem to avoid relying on only one arbitrary data clustering choice. We consider new ways to control the occurrence of false discoveries with a given spatial tolerance. This control is well adapted to spatially structured data. In this work, we focus on neuroimaging datasets but the methods that we present can be adapted to other fields which share similar setups
Dumont, Grégory. "Analyse de modèles de population de neurones : cas des neurones à réponse postsynaptique par saut de potentiel." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14601/document.
Повний текст джерелаThis thesis concerns the mathematical modelling and the study of the behavior of a population of neurons. In this work we will mainly consider a population of excitatory neurons whe reall the cells of the network follow the integrate-and-fire model. Nonetheless, we will tackle in a chapter the modelling of an inhibitory population of neurons, and we will discuss in the lastchapter the modelling of a population of neurons that follows the Ermentrout-Koppell model.The point of view of this thesis is given by the population density approach that has beenintroduced more than a decade ago in order to facilitate the simulation of a large assembly ofneurons. More precisely, this approach gives a partial differential equation that describes thedensity of neurons in the state space that is the set of all admissible potential of a neuron. We will assume that when receiving an action potential, the potential of the neuron makes a small jump. As we will see this partial differential equation is non linear (due to the coupling betweenneurons) and non-local (due to the potential jump). If this idea is complicated and abstract, itallows to simulate easily a large neural network.First of all, the thesis gives a mathematical framework for the equations that arise from thisthe population density approach. Then we will discuss the existence and the possible blow upin finite time of the solution. We will discuss how the consideration of more realistic modellingassumptions, as the refractory period and the delay between the emission and the reception ofan action potential can stop the blow up of the solution and give a well posed model.We will also try to caracterise the occurence of synchronization of the neural network. Twodifferent ways of seeing the synchronization will be describe. One relates the blow up in finitetime of the solution to the occurence of a Dirac mass in the firing rate of the population.Nonetheless, taking into account the delays, this kind of blow up will not be observed anymore.Nonetheless, as we will see, with this additional features the model will generate some periodicalsolutions that can also be related to the synchronization of the population
Dora, Matteo. "Mathematical models and signal processing methods to explore biological mechanisms across multiple scales : from intracellular dynamics to neural time series." Electronic Thesis or Diss., Université Paris sciences et lettres, 2022. http://www.theses.fr/2022UPSLE033.
Повний текст джерелаThis dissertation is an investigation of biological phenomena related to the brain by means of mathematical models and quantitative methods. The leitmotiv of this work is the analysis of spatiotemporal series which naturally arise in biological systems at different scales. In the first part of the thesis, we study the finest of such scales. I analyse intracellular protein dynamics in the endoplasmic reticulum (ER), an organelle of eukaryotic cells formed by a network of tubular membrane structures. The ER plays a key role in protein transport, and its dysfunction has been associated with numerous diseases, including, in particular, neurodegenerative disorders. Previous experimental observations suggested a possible deviation of ER luminal transport compared to classical diffusion. Based on this hypothesis, I introduce a graph model to describe ER protein dynamics. I analyse the model and develop numerical simulations, revealing a possible mechanism of aggregated protein transport that deviates from purely diffusive motion. Then, to further test the predictions of the model, we turn to the analysis of experimental data. While protein mobility has been traditionally characterized by fluorescence imaging, the morphological characteristics of the ER pose new challenges to a quantitative analysis of such small scale dynamics. To address these issues, I introduce a novel image processing method to analyse ER dynamics based on photoactivatable fluorescent proteins. By joining analysis and reduction of noise with automatic segmentation of the ER, the technique can provide a robust estimation of the timescale of transport. Moreover, it allows us to characterize the spatial heterogeneity of the protein mixing process. I present and compare results for luminal, membrane, and misfolded proteins in the ER. In the second part of the dissertation, we study neuronal signals at coarser scales. First, at the scale of the single neuron, I present a denoising method suitable for optical recording of single-cell activity in awake, behaving mice via fluorescent voltage indicators. I show how it is possible to reduce instrumental and photon-counting noise in such time series, allowing us to extract spike patterns at lower acquisition frequency. Such results enable simultaneous recording of multiple cells, thus allowing to explore the correlation of spikes and voltage oscillations within ensembles of neurons. Finally, in the last chapters, we reach the coarsest scale with the study of electroencephalograms (EEG) which record the activity of the entire brain. Motivated by the applications of EEG in clinical monitoring, I introduce a new wavelet-based method that can attenuate undesired artefacts which contaminate the recording of the physiological EEG signal. The method is based on the remapping of the wavelet coefficients according to a reference distribution extracted from clean portions of the EEG signal. This technique can provide a flexible alternative to traditional approaches such as wavelet thresholding in the context of real-time clinical monitoring. In conclusion, this thesis illustrates how an interdisciplinary approach combining experimental data with mathematical modelling and signal processing can provide new tools for the understanding of a wide variety of biological mechanisms, ranging from protein transport to EEG monitoring
Maumet, Camille. "From group to patient-specific analysis of brain function in arterial spin labelling and BOLD functional MRI." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00863908.
Повний текст джерелаRio, Maxime. "Modèles bayésiens pour la détection de synchronisations au sein de signaux électro-corticaux." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0090.
Повний текст джерелаThis thesis promotes new methods to analyze intracranial cerebral signals (local field potentials), which overcome limitations of the standard time-frequency method of event-related spectral perturbations analysis: averaging over the trials and relying on the activity in the pre-stimulus period. The first proposed method is based on the detection of sub-networks of electrodes whose activity presents cooccurring synchronisations at a same point of the time-frequency plan, using bayesian gaussian mixture models. The relevant sub-networks are validated with a stability measure computed over the results obtained from different trials. For the second proposed method, the fact that a white noise in the temporal domain is transformed into a rician noise in the amplitude domain of a time-frequency transform made possible the development of a segmentation of the signal in each frequency band of each trial into two possible levels, a high one and a low one, using bayesian rician mixture models with two components. From these two levels, a statistical analysis can detect time-frequency regions more or less active. To develop the bayesian rician mixture model, new algorithms of variational bayesian inference have been created for the Rice distribution and the rician mixture distribution. Performances of the new methods have been evaluated on artificial data and experimental data recorded on monkeys. It appears that the new methods generate less false positive results and are more robust to a lack of data in the pre-stimulus period
Rio, Maxime. "Modèles bayésiens pour la détection de synchronisations au sein de signaux électro-corticaux." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00859307.
Повний текст джерелаScherrer, Benoît. "Segmentation des tissus et structures sur les IRM cérébrales : agents markoviens locaux coopératifs et formulation bayésienne." Phd thesis, 2008. http://tel.archives-ouvertes.fr/tel-00361317.
Повний текст джерелаLa localité est modélisée via un cadre multi-agents : des agents sont distribués dans le volume et réalisent une segmentation markovienne locale. Dans une première approche (LOCUS, Local Cooperative Unified Segmentation) nous proposons des mécanismes intuitifs de coopération et de couplage pour assurer la cohérence des modèles locaux. Les structures sont segmentées via l'intégration de contraintes de localisation floue décrites par des relations spatiales entre structures. Dans une seconde approche (LOCUS-B, LOCUS in a Bayesian framework) nous considérons l'introduction d'un atlas statistique des structures. Nous reformulons le problème dans un cadre bayésien nous permettant une formalisation statistique du couplage et de la coopération. Segmentation des tissus, régularisation des modèles locaux, segmentation des structures et recalage local affine de l'atlas sont alors réalisés de manière couplée dans un cadre EM, chacune des étapes s'améliorant mutuellement.
L'évaluation sur des images simulées et réelles montrent les performances de l'approche et en particulier sa robustesse aux artéfacts pour de faibles temps de calculs. Les modèles markoviens locaux distribués et coopératifs apparaissent alors comme une approche prometteuse pour la segmentation d'images médicales.