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Academic literature on the topic 'Dendrites (cytologie) – Modèles mathématiques'
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Dissertations / Theses on the topic "Dendrites (cytologie) – Modèles mathématiques"
Dubois-Boissier, Marie-Dominique. "Modélisation d'un neurone du striatum." Université Joseph Fourier (Grenoble), 1996. http://www.theses.fr/1996GRE19004.
Full textLajeunesse, Francis. "Modélisation de l'intégration des entrées synaptiques excitatrices chez les cellules thalamocorticales." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28114/28114.pdf.
Full textThalamocortical (TC) cells from the ventroposterolateral (VPL) nucleus of the thalamus relay the somatosensory inputs (excitatory lemniscal synapses at proximal dendrites) to the corresponding cortical area, but also receive feedback excitatory inputs from the cortex (corticothalamic synapses at distal dendrites). The goal of this study was to compare the synaptic integration of inputs coming to proximal vs. distal dendrites. A multicompartmental model was drawn from fully reconstructed cells of the VPL nucleus. Dendrites were spatially discretized in multiple segments associated to interconnected RC circuits. We were able to characterize the impact of neuronal size and dendritic diameter on the amplitude and on the time course of the somatic response. We also compared the synaptic integration for different distributions of proximal or distal inputs under different conditions of membrane potential and active properties. In all cases, the summation of proximal inputs was independent of their distribution, while the response induced by distal inputs saturated when those inputs were located at the same branches. The results obtained in this study suggest a physiological explanation of the synaptic pattern at TC cells.
Guinaudeau, Ophélie. "Neurone abstrait : une formalisation de l’intégration dendritique et ses propriétés algébriques." Thesis, Université Côte d'Azur (ComUE), 2019. http://www.theses.fr/2019AZUR4001/document.
Full textBiological neurons communicate by means of electrical impulses, called spikes. Brain functions emerge notably from reception and emission coordination between those spikes. Furthermore, it is widely accepted that the function of each neuron depends on its morphology. In particular, dendrites perform the spatio-temporal integration of received spikes and affect the occurrence of emitted spikes. Dendrites are therefore fundamental for in silico studies of coordination mechanisms, and especially for the study of so-called neuron assemblies. Most of existing neuron models taking into account dendrites are detailed mathematical models, usually based on differential equations, whose simulations require significant computing resources. Moreover, their intrinsic complexity makes difficult the analysis and proofs on such models. In this thesis, we propose an abstract neuron model integrating dendrites. In order to pave the way to formal methods, we establish a rigorous definition of the modeling framework and highlight remarkable algebraic properties of dendritic integration. In particular, we have demonstrated that it is possible to reduce a neuron structure while preserving its input/output function. We have thus revealed equivalence classes with a canonical representative. Based on category theory and thanks to properly defined neuron morphisms, we then analyzed these equivalence classes in more details. A surprising result derives from these properties: simply adding delays in neuron computational models is sufficient to represent an abstract dendritic integration, without explicit tree structure representation of dendrites. At the root of the dendritic tree, soma modeling inevitably contains a differential equation in order to preserve the biological functioning essence. This requires combining an analytical vision with the algebraic vision. Nevertheless, thanks to a preliminary step of temporal discretization, we have also implemented a complete neuron in Lustre which is a formal language allowing proofs by model checking. All in all, we bring in this thesis an encouraging first step towards a complete neuron formalization, with remarkable properties on dendritic integration
Paragot, Paul. "Analyse numérique du système d'équations Poisson-Nernst Planck pour étudier la propagation d'un signal transitoire dans les neurones." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5020.
Full textNeuroscientific questions about dendrites include understanding their structural plasticityin response to learning and how they integrate signals. Researchers aim to unravel these aspects to enhance our understanding of neural function and its complexities. This thesis aims at offering numerical insights concerning voltage and ionic dynamics in dendrites. Our primary focus is on modeling neuronal excitation, particularly in dendritic small compartments. We address ionic dynamics following the influx of nerve signals from synapses, including dendritic spines. To accurately represent their small scale, we solve the well-known Poisson-Nernst-Planck (PNP) system of equations, within this real application. The PNP system is widely recognized as the standard model for characterizing the electrodiffusion phenomenon of ions in electrolytes, including dendritic structures. This non-linear system presents challenges in both modeling and computation due to the presence of stiff boundary layers (BL). We begin by proposing numerical schemes based on the Discrete Duality Finite Volumes method (DDFV) to solve the PNP system. This method enables local mesh refinement at the BL, using general meshes. This approach facilitates solving the system on a 2D domain that represents the geometry of dendritic arborization. Additionally, we employ numerical schemes that preserve the positivity of ionic concentrations. Chapters 1 and 2 present the PNP system and the DDFV method along with its discrete operators. Chapter 2 presents a "linear" coupling of equations and investigate its associated numerical scheme. This coupling poses convergence challenges, where we demonstrate its limitations through numerical results. Chapter 3 introduces a "nonlinear" coupling, which enables accurate numerical resolution of the PNP system. Both of couplings are performed using DDFV method. However, in Chapter 3, we demonstrate the accuracy of the DDFV scheme, achieving second-order accuracy in space. Furthermore, we simulate a test case involving the BL. Finally, we apply the DDFV scheme to the geometry of dendritic spines and discuss our numerical simulations by comparing them with 1D existing simulations in the literature. Our approach considers the complexities of 2D dendritic structures. We also introduce two original configurations of dendrites, providing insights into how dendritic spines influence each other, revealing the extent of their mutual influence. Our simulations show the propagation distance of ionic influx during synaptic connections. In Chapter 4, we solve the PNP system over a 2D multi-domain consisting of a membrane, an internal and external medium. This approach allows the modeling of voltage dynamics in a more realistic way, and further helps checking consistency of the results in Chapter 3. To achieve this, we employ the FreeFem++ software to solve the PNP system within this 2D context. We present simulations that correspond to the results obtained in Chapter 3, demonstrating linear summation in a dendrite bifurcation. Furthermore, we investigate signal summation by adding inputs to the membrane of a dendritic branch. We identify an excitability threshold where the voltage dynamics are significantly influenced by the number of inputs. Finally, we also offer numerical illustrations of the BL within the intracellular medium, observing small fluctuations. These results are preliminary, aiming to provide insights into understanding dendritic dynamics. Chapter 5 presents collaborative work conducted during the Cemracs 2022. We focus on a composite finite volume scheme where we aim to derive the Euler equations with source terms on unstructured meshes
Zomorrodi, Moghaddam Reza. "Influence of the dentritic morphology on electrophysiological responses of thalamocortical neurons." Doctoral thesis, Université Laval, 2011. http://hdl.handle.net/20.500.11794/22954.
Full textThalamic relay neurons have an exclusive role in processing and transferring nearly all sensory information into the cortex. The synaptic integration and the electrophysiological response of thalamic relay neurons are determined not only by a state of the involved network, but they are also controlled by their intrinsic properties; such as diverse voltage-dependent ionic channels as well as by elaborated dendritic arborization. Therefore, investigating the complex pattern of dendritic morphology and dendritic active properties reveals important information on the input-output function of thalamic relay neurons. In this study, we reconstructed eight thalamocortical (TC) neurons from the VPL nucleus of adult cats. Based on these complete morphological data, we developed several multi-compartment models in order to find a potentially important role for dendritic trees of TC neurons in the synaptic integration and neuronal computation. The analysis of morphological features of TC neurons yield precise values of geometrical parameters either similar or different from those previously reported. In addition, this analysis extracted new information regarding the pattern of connectivity between dendritic sections such as asymmetry index and mean path length (i.e., topological parameters). We confirmed the same range of previously reported value for several geometric parameters such as the somatic area (2956.24±918.89 m2), the total dendritic length (168017.49±4364.64 m) and the number of subtrees (8.3±1.5) for eight TC neurons. However, contrary to previously reported data, the dendritic branching pattern (with 98% bifurcation cases) does not follow Rall’s 3/2 power rule for the geometrical ratio (GR), and the average GR value for a forward propagation signal was 2.5 times bigger than for a backward propagating signal. We also demonstrated a significant variability in the symmetry index between subtrees of TC neurons, but the mean path length did not show a large variation through the dendritic arborizations of different neurons. We examined the consequence of non-uniform distribution of T-channels along the dendritic tree on the prominent electrophysiological response, the low-threshold Ca2+ spike (LTS) of TC neurons. By applying the hypothesis of “minimizing metabolic cost”, we found that the modeled neuron needed a minimum number of T-channels to generate low-threshold Ca2+ spike (LTS), when T-channels were located in proximal dendrites. In the next study, our computational model illustrated the extent of an action potential back propagation and the efficacy of forward propagation of EPSPs arriving at the distal dendritic branches. We demonstrated that dendritic propagation of electrical signals is strongly controlled by morphological parameters as shown by different levels of polarization achieved by a neuron at equidistance from the soma during back and forward propagation of electrical signals. Our results revealed that geometrical properties (i.e. diameter, GR) have a stronger impact on the electrical signal propagation than topological properties. We conclude that (1) diversity in the morphological properties between subtrees of a single TC neuron lead to a specific ability for synaptic integration and neuronal computation of different dendrites, (2) geometrical parameter of a dendritic tree provide higher influence on the control of synaptic efficacy and the extent of the back propagating action potential than topological properties, (3) TC neurons follow the optimized principle for distribution of voltage-dependent conductance on dendritic trees.
Dimitrio, Luna. "MODELLING NUCLEOCYTOPLASMIC TRANSPORT WITH APPLICATION TO THE INTRACELLULAR DYNAMICS OF THE TUMOR SUPPRESSOR PROTEIN P53." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00769901.
Full textRasheed, Amer. "Solidification Dendritique de Mélanges Binaires de Métaux sous l'Action de Champs Magnétique: Modélisation, Analyse Mathématique et Numérique." Phd thesis, INSA de Rennes, 2010. http://tel.archives-ouvertes.fr/tel-00565743.
Full textRua, Ferreira Rita. "Etude du mécanisme de la sensation du flux ciliaire dans l'organiseur droite gauche du poisson zèbre." Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAJ001/document.
Full textBoth motile and immotile cilia play important roles in left-right (LR) axis determination, which generally involves cilia-mediated directional flows in organized structures (LR organizers, LRO) in which the LR symmetry is broken, thus driving asymmetric organogenesis in the developing embryos. In my PhD project we aimed to developed a method (3D-Cilia Map) and analyze the three-dimensional organization of ciliary implantation in order to extract the key parameters modulating the directional flow involved in breaking the axis of symmetry in the zebra fish LRO. Altogether, our results suggest the initial mechanism to break the LR symmetry is most likely to be based on the transport of achemical signal, while later, cells intrinsically provide their cilia the cues to orient asymmetrically. The work presented here represents an important contribution to our current understanding of cilia behaviors and flow-sensing mechanisms in the establishment of the left-right axis in the zebra fish LRO
Books on the topic "Dendrites (cytologie) – Modèles mathématiques"
Suzanne, Tyc-Dumont, ed. Le neurone computationnel: Histoire d'un siècle de recherches. Paris: CNRS, 2005.
Find full textThe Systems Biology Workbook A Handson Introduction To A Revolution In Biology. CRC Press, 2010.
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