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

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

Time-delayed feedback in neurosystems

The influence of time delay in systems of two coupled excitable neurons is studied in the framework of the FitzHugh-Nagumo model. Time-delay can occur in the coupling between neurons or in a self-feedback loop. The stochastic synchronization of instantaneously coupled neurons under the influence of white noise can be deliberately controlled by local time-delayed feedback. By appropriate choice of the delay time synchronization can be either enhanced or suppressed. In delay-coupled neurons, antiphase oscillations can be induced for sufficiently large delay and coupling strength. The additional application of time-delayed self-feedback leads to complex scenarios of synchronized in-phase or antiphase oscillations, bursting patterns, or amplitude death.Comment: 13 pages, 13 figure

Triggering up states in all-to-all coupled neurons

Slow-wave sleep in mammalians is characterized by a change of large-scale cortical activity currently paraphrased as cortical Up/Down states. A recent experiment demonstrated a bistable collective behaviour in ferret slices, with the remarkable property that the Up states can be switched on and off with pulses, or excitations, of same polarity; whereby the effect of the second pulse significantly depends on the time interval between the pulses. Here we present a simple time discrete model of a neural network that exhibits this type of behaviour, as well as quantitatively reproduces the time-dependence found in the experiments.Comment: epl Europhysics Letters, accepted (2010

Prefrontal Parvalbumin Neurons in Control of Attention

SummaryWhile signatures of attention have been extensively studied in sensory systems, the neural sources and computations responsible for top-down control of attention are largely unknown. Using chronic recordings in mice, we found that fast-spiking parvalbumin (FS-PV) interneurons in medial prefrontal cortex (mPFC) uniformly show increased and sustained firing during goal-driven attentional processing, correlating to the level of attention. Elevated activity of FS-PV neurons on the timescale of seconds predicted successful execution of behavior. Successful allocation of attention was characterized by strong synchronization of FS-PV neurons, increased gamma oscillations, and phase locking of pyramidal firing. Phase-locked pyramidal neurons showed gamma-phase-dependent rate modulation during successful attentional processing. Optogenetic silencing of FS-PV neurons deteriorated attentional processing, while optogenetic synchronization of FS-PV neurons at gamma frequencies had pro-cognitive effects and improved goal-directed behavior. FS-PV neurons thus act as a functional unit coordinating the activity in the local mPFC circuit during goal-driven attentional processing

Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model

Published April 17, 2014Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity.Mark D. McDonnell, Lawrence M. War

Effects of ionic concentration dynamics on neuronal activity

Neuronen sind bei der Informationsübertragung des zentralen Nervensystems von entscheidender Bedeutung. Ihre Aktivität liegt der Signalverarbeitung und höheren kognitiven Prozessen zugrunde. Neuronen sind in den extrazellulären Raum eingebettet, der mehrere Teilchen, darunter auch Ionen, enthält. Ionenkonzentrationen sind nicht statisch. Intensive neuronale Aktivität kann intrazelluläre und extrazelluläre Ionenkonzentrationen verändern. In dieser Arbeit untersuche ich das Wechselspiel zwischen neuronaler Aktivität und der Dynamik der Ionenkonzentrationen. Dabei konzentriere ich mich hauptsächlich auf extrazelluläre Kalium- und intrazelluläre Natriumkonzentrationen. Mit Hilfe der Theorie dynamischer Systeme zeige ich, wie moderate Änderungen dieser Ionenkonzentrationen die neuronale Aktivität qualitativ verändern können, wodurch sich möglicherweise die Signalverarbeitung verändert. Dann modelliere ich ein leitfähigkeitsbasiertes neuronales Netzwerk mit Spikes. Das Modell sagt voraus, dass eine moderate Änderung der Konzentrationen, die einen Mikroschaltkreis von Neuronen umgeben, die Leistungsspektraldichte der Populationsaktivität verändern könnte. Insgesamt unterstreicht diese Arbeit die Bedeutung der Dynamik der Ionenkonzentrationen für das Verständnis neuronaler Aktivität auf langen Zeitskalen und liefert technische Erkenntnisse darüber, wie das Zusammenspiel zwischen ihnen modelliert und analysiert werden kann.Neurons are essential in the information transfer mechanisms of the central nervous system. Their activity underlies both basic signal processing, and higher cognitive processes. Neurons are embedded in the extracellular space, which contains multiple particles, including ions which are vital to their functioning. Ionic concentrations are not static, intense neuronal activity alters the intracellular and extracellular ionic concentrations which in turn affect neuronal functioning. In this thesis, I study the interplay between neuronal activity and ionic concentration dynamics. I focus specifically on the extracellular potassium and intracellular sodium concentrations. Using dynamical systems theory, I illustrate how moderate changes in these ionic concentrations can qualitatively change neuronal activity, potentially altering signal processing. I then model a conductance-based spiking neural network. The model predicts that a moderate change in the concentrations surrounding a microcircuit of neurons could modify the power spectral density of the population activity. Altogether, this work highlights the need to consider ionic concentration dynamics to understand neuronal activity on long time scales and provides technical insights on how to model and analyze the interplay between them