898 research outputs found

    Modeling Maintenance of Long-Term Potentiation in Clustered Synapses, Long-Term Memory Without Bistability

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    Memories are stored, at least partly, as patterns of strong synapses. Given molecular turnover, how can synapses maintain strong for the years that memories can persist? Some models postulate that biochemical bistability maintains strong synapses. However, bistability should give a bimodal distribution of synaptic strength or weight, whereas current data show unimodal distributions for weights and for a correlated variable, dendritic spine volume. Bistability of single synapses has also never been empirically demonstrated. Thus it is important for models to simulate both unimodal distributions and long-term memory persistence. Here a model is developed that connects ongoing, competing processes of synaptic growth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term (in excess of 1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved essential to incorporate resource competition between synapses organized into small clusters. With competition, these clusters are stable for years. These simulations concur with recent data to support the clustered plasticity hypothesis, which suggests clusters, rather than single synaptic contacts, may be a fundamental unit for storage of long-term memory. The model makes empirical predictions, and may provide a framework to investigate mechanisms maintaining the balance between synaptic plasticity and stability of memory.Comment: 17 pages, 5 figure

    A simple transfer function for nonlinear dendritic integration

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    Relatively recent advances in patch clamp recordings and iontophoresis have enabled unprecedented study of neuronal post-synaptic integration (dendritic integration). Findings support a separate layer of integration in the dendritic branches before potentials reach the cell’s soma. While integration between branches obeys previous linear assumptions, proximal inputs within a branch produce threshold nonlinearity, which some authors have likened to the sigmoid function. Here we show the implausibility of a sigmoidal relation and present a more realistic transfer function in both an elegant artificial form and a biophysically derived form that further considers input locations along the dendritic arbor. As the distance between input locations determines their ability to produce nonlinear interactions, models incorporating dendritic topology are essential to understanding the computational power afforded by these early stages of integration. We use the biophysical transfer function to emulate empirical data using biophysical parameters and describe the conditions under which the artificial and biophysically derived forms are equivalent

    Intrinsic and synaptic membrane properties of neurons in the thalamic reticular nucleus

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    Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2004-2005Le noyau réticulaire thalamique (RE) est une structure qui engendre des fuseaux, une oscillation bioélectrique de marque pendant les stades précoces du sommeil. De multiples propriétés neuronales, intrinsèques et synaptiques, sont impliquées dans la génération, la propagation, le maintien et la terminaison des ondes en fuseaux. D’un autre côté, ce rythme constitue un état spécial de l’activité du réseau qui est généré par le réseau lui-même et affecte les propriétés cellulaires du noyau RE. Cette étude se concentre sur ces sujets: comment les propriétés cellulaires et les propriétés du réseau sont inter-reliées et interagissent pour engendrer les ondes fuseaux dans les neurones du RE et leurs cibles, les neurones thalamocorticaux. La présente thèse fournit de nouvelles évidences montrant le rôle fondamental joué par les neurones du noyau RE dans la genèse des ondes en fuseaux, dû aux synapses chimiques établies par ces neurones. La propagation et la synchronisation de l’activité sont modulées par les synapses électriques entre les neurones réticulaires thalamiques, mais aussi par les composantes dépolarisantes secondaires des réponses synaptiques évoquées par le cortex. De plus, la forme générale et la terminaison des oscillations thalamiques sont probablement contrôlées en grande partie par les neurones du RE, lesquels expriment une conductance intrinsèque leurs procurant une membrane avec un comportement bistable. Finalement, les oscillations thalamiques en fuseaux sont aussi capables de moduler les propriétés membranaires et l’activité des neurones individuels du RE.The thalamic reticular nucleus (RE) is a key structure related to spindles, a hallmark bioelectrical oscillation during early stages of sleep. Multiple neuronal properties, both intrinsic and synaptic, are implicated in the generation, propagation, maintenance and termination of spindle waves. On the other hand, this rhythm constitutes a special state of network activity, which is generated within, and affects single-cell properties of the RE nucleus. This study is focused on these topics: how cellular and network properties are interrelated and interact to generate spindle waves in the pacemaking RE neurons and their targets, thalamocortical neurons. The present thesis provides new evidence showing the fundamental role played by the RE nucleus in the generation of spindle waves, due to chemical synapses established by its neurons. The propagation and synchronization of activity is modulated by electrical synapses between thalamic reticular neurons, but also by the secondary depolarizing component of cortically-evoked synaptic responses. Additionally, the general shaping and probably the termination of thalamic oscillations could be controlled to a great extent by RE neurons, which express an intrinsic conductance endowing them with membrane bistable behaviour. Finally, thalamic spindle oscillations are also able to modulate the membrane properties and activities of individual RE neurons

    Robust short-term memory without synaptic learning

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    Short-term memory in the brain cannot in general be explained the way long-term memory can -- as a gradual modification of synaptic weights -- since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.Comment: 20 pages, 9 figures. Amended to include section on spiking neurons, with general rewrit

    Vortex avalanches and magnetic flux fragmentation in superconductors

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    We report results of numerical simulations of non isothermal dendritic flux penetration in type-II superconductors. We propose a generic mechanism of dynamic branching of a propagating hotspot of a flux flow/normal state triggered by a local heat pulse. The branching occurs when the flux hotspot reflects from inhomogeneities or the boundary on which magnetization currents either vanish, or change direction. Then the hotspot undergoes a cascade of successive splittings, giving rise to a dissipative dendritic-type flux structure. This dynamic state eventually cools down, turning into a frozen multi-filamentary pattern of magnetization currents.Comment: 4 pages, 4 figures, accepted to Phys. Rev. Let
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