309 resultados para Dendrites


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Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

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Thesis (Ph.D.)--University of Washington, 2016-08

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Thesis (Ph.D.)--University of Washington, 2016-08

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L’inflammation du système nerveux central (SNC), appelée neuroinflammation, est un aspect inséparable des maladies neurodégénératives chroniques comme la sclérose en plaques (SEP) et la maladie d’Alzheimer (MA). La caractérisation de la signature moléculaire spécifique à chaque population cellulaire dans des pathologies distinctes va aboutir à la compréhension et donc au contrôle de la neuroinflammation. Le présent ouvrage a pour but de mieux comprendre les mécanismes d’action de deux types cellulaires myéloïdes, la microglie et les neutrophiles, au cours des affections neuroinflammatoires du SNC. Ainsi, le premier objectif a été de comprendre le rôle des cytokines IL-36 dans la neuroinflammation établie au cours de l’encéphalomyélite auto-immune expérimentale (EAE). Dans une seconde partie, l’objectif a été d’explorer l’action du GPR84, un récepteur couplé à la protéine G spécifique à la microglie dans le SNC, lors de l’altération des fonctions cérébrales dans un modèle de souris transgénique de la MA. Nos résultats démontrent que la voie de signalisation IL-36/IL36R est augmentée dans trois modèles différents de l’EAE, mais ne contribue pas au développement ni à la progression de la pathologie. En utilisant l’approche de cytométrie en flux nous identifions les neutrophiles comme la source majeure de l’IL-36γ. De plus, nous démontrons que la microglie exprime l’IL-36R et sa stimulation par l’IL-36γ conduit à la production de cytokines pro-inflammatoires. Dans un second temps, nous caractérisons l’augmentation de l’expression du GPR84 par la microglie dans le modèle murin de la MA APP/PS1. Ainsi, le croisement de ces souris avec des souris déficientes en GPR84 diminue l’activation et le recrutement de la microglie autour des plaques d’amyloïde-β et accélère le déclin cognitif. Nos études impliquent le GPR84 comme un acteur important dans le maintien de l’homéostasie neuronale puisque son absence favorise la dégénérescence des dendrites dans le cerveau. Les résultats obtenus dans cette thèse apportent de nouveaux éléments qui peuvent contribuer au développement des thérapies qui ciblent les cellules myéloïdes dans diverses pathologies du SNC. Ces données ouvrent de nouvelles pistes pour élucider le rôle de l’IL-36γ dans des maladies neurodégénératives. Enfin, pour une première fois, nous présentons un modèle murin permettant d’identifier le(s) ligand(s) endogène(s) du GPR84, une cible thérapeutique potentielle pour la prévention et/ou le traitement de la MA.

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We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations, as well as coincidence detection in pairs of dendrites and the dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools that will ultimately underpin systems neuroscience.

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In this paper we consider a class of scalar integral equations with a form of space-dependent delay. These non-local models arise naturally when modelling neural tissue with active axons and passive dendrites. Such systems are known to support a dynamic (oscillatory) Turing instability of the homogeneous steady state. In this paper we develop a weakly nonlinear analysis of the travelling and standing waves that form beyond the point of instability. The appropriate amplitude equations are found to be the coupled mean-field Ginzburg-Landau equations describing a Turing-Hopf bifurcation with modulation group velocity of O(1). Importantly we are able to obtain the coefficients of terms in the amplitude equations in terms of integral transforms of the spatio-temporal kernels defining the neural field equation of interest. Indeed our results cover not only models with axonal or dendritic delays but those which are described by a more general distribution of delayed spatio-temporal interactions. We illustrate the predictive power of this form of analysis with comparison against direct numerical simulations, paying particular attention to the competition between standing and travelling waves and the onset of Benjamin-Feir instabilities.

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Understanding the mode-locked response of excitable systems to periodic forcing has important applications in neuroscience. For example it is known that spatially extended place cells in the hippocampus are driven by the theta rhythm to generate a code conveying information about spatial location. Thus it is important to explore the role of neuronal dendrites in generating the response to periodic current injection. In this paper we pursue this using a compartmental model, with linear dynamics for each compartment, coupled to an active soma model that generates action potentials. By working with the piece-wise linear McKean model for the soma we show how the response of the whole neuron model (soma and dendrites) can be written in closed form. We exploit this to construct a stroboscopic map describing the response of the spatially extended model to periodic forcing. A linear stability analysis of this map, together with a careful treatment of the non-differentiability of the soma model, allows us to construct the Arnol'd tongue structure for 1:q states (one action potential for q cycles of forcing). Importantly we show how the presence of quasi-active membrane in the dendrites can influence the shape of tongues. Direct numerical simulations confirm our theory and further indicate that resonant dendritic membrane can enlarge the windows in parameter space for chaotic behavior. These simulations also show that the spatially extended neuron model responds differently to global as opposed to point forcing. In the former case spatio-temporal patterns of activity within an Arnol'd tongue are standing waves, whilst in the latter they are traveling waves.

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International audience

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A solid state lithium metal battery based on a lithium garnet material was developed, constructed and tested. Specifically, a porous-dense-porous trilayer structure was fabricated by tape casting, a roll-to-roll technique conducive to high volume manufacturing. The high density and thin center layer (< 20 μm) effectively blocks dendrites even over hundreds of cycles. The microstructured porous layers, serving as electrode supports, are demonstrated to increase the interfacial surface area available to the electrodes and increase cathode loading. Reproducibility of flat, well sintered ceramics was achieved with consistent powderbed lattice parameter and ball milling of powderbed. Together, the resistance of the LLCZN trilayer was measured at an average of 7.6 ohm-cm2 in a symmetric lithium cell, significantly lower than any other reported literature results. Building on these results, a full cell with a lithium metal anode, LLCZN trilayer electrolyte, and LiCoO2 cathode was cycled 100 cycles without decay and an average ASR of 117 ohm-cm2. After cycling, the cell was held at open circuit for 24 hours without any voltage fade, demonstrating the absence of a dendrite or short-circuit of any type. Cost calculations guided the optimization of a trilayer structure predicted that resulting cells will be highly competitive in the marketplace as intrinsically safe lithium batteries with energy densities greater than 300 Wh/kg and 1000 Wh/L for under $100/kWh. Also in the pursuit of solid state batteries, an improved Na+ superionic conductor (NASICON) composition, Na3Zr2Si2PO12, was developed with a conductivity of 1.9x10-3 S/cm. New super-lithiated lithium garnet compositions, Li7.06La3Zr1.94Y0.06O12 and Li7.16La3Zr1.84Y0.16O12, were developed and studied revealing insights about the mechanisms of conductivity in lithium garnets.