20 research outputs found

    Switchable slow cellular conductances determine robustness and tunability of network states.

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    Neuronal information processing is regulated by fast and localized fluctuations of brain states. Brain states reliably switch between distinct spatiotemporal signatures at a network scale even though they are composed of heterogeneous and variable rhythms at a cellular scale. We investigated the mechanisms of this network control in a conductance-based population model that reliably switches between active and oscillatory mean-fields. Robust control of the mean-field properties relies critically on a switchable negative intrinsic conductance at the cellular level. This conductance endows circuits with a shared cellular positive feedback that can switch population rhythms on and off at a cellular resolution. The switch is largely independent from other intrinsic neuronal properties, network size and synaptic connectivity. It is therefore compatible with the temporal variability and spatial heterogeneity induced by slower regulatory functions such as neuromodulation, synaptic plasticity and homeostasis. Strikingly, the required cellular mechanism is available in all cell types that possess T-type calcium channels but unavailable in computational models that neglect the slow kinetics of their activation

    Cellular switches orchestrate rhythmic circuits.

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    Small inhibitory neuronal circuits have long been identified as key neuronal motifs to generate and modulate the coexisting rhythms of various motor functions. Our paper highlights the role of a cellular switching mechanism to orchestrate such circuits. The cellular switch makes the circuits reconfigurable, robust, adaptable, and externally controllable. Without this cellular mechanism, the circuit rhythms entirely rely on specific tunings of the synaptic connectivity, which makes them rigid, fragile, and difficult to control externally. We illustrate those properties on the much studied architecture of a small network controlling both the pyloric and gastric rhythms of crabs. The cellular switch is provided by a slow negative conductance often neglected in mathematical modeling of central pattern generators. We propose that this conductance is simple to model and key to computational studies of rhythmic circuit neuromodulation

    Control by neuromodulation: A tutorial

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    This tutorial provides an introduction to the topic of neuromodulation as an important control paradigm for natural and artificial neuronal networks. We review how neuromodulation modulates excitability, and how neuromodulation interacts with homeostasis. We stress how modulating nodal excitability provides a robust and versatile control principle to dynamically reconfigure the connectivity of rhythmic circuits and to shape the spatio-temporal synchrony of large populations.ER

    Control Across Scales by Positive and Negative Feedback

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    Feedback is a key element of regulation, as it shapes the sensitivity of a process to its environment. Positive feedback upregulates, and negative feedback downregulates. Many regulatory processes involve a mixture of both, whether in nature or in engineering. This article revisits the mixed-feedback paradigm, with the aim of investigating control across scales. We propose that mixed feedback regulates excitability and that excitability plays a central role in multiscale neuronal signaling. We analyze this role in a multiscale network architecture inspired by neurophysiology. The nodal behavior defines a mesoscale that connects actuation at the microscale to regulation at the macroscale. We show that mixed-feedback nodal control provides regulatory principles at the network scale, with a nodal resolution. In this sense, the mixed-feedback paradigm is a control principle across scales. </jats:p

    Analyse de sensibilité et de robustesse des modèles de neurone du thalamus à l'échelle cellulaire et réseau

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    In the early fties, Hodgkin and Huxley developed a model of the electrical activity of the neuron. Based on a simple RC circuit with non-linear conductances, they reproduced very well the electrical behavior of a squid neuron. Over the last fifty years, thanks to the increase of experimental data and knowledge in neuroscience, scientists have extended the Hodgkin and Huxley's model to more complex neurons. But they have often increased the complexity which makes their models less robust. This thesis focuses on thalamic neurons. The thalamus is the relay-station for the sensory inputs travelling to the cortex. Depending on the state, the thalamic neurons exhibit two different firing patterns. During sleep, the neurons are bursting, which stops the information processing. During wakefulness, the neurons are spiking and the thalamus processes the inputs. In order to study diseases such as absence seizures in children, or to describe more precisely the thalamic behavior, a robust model of neuron activity switch is necessary. This robustness has to be maintained when the study is performed at the network level. Recent evidences have highlighted the critical role of the slow dynamics of neuronal calcium currents in the switch from spiking to bursting. Inspired by this line of work, this thesis gathers conductance-based models of thalamic neuron in the literature. The major difference between them is the presence of the slow kinetics of the calcium current. The first contribution is their robustness comparison at the cellular level. Models which lack this slow dynamic are fragile when they are subjected to perturbation. The second contribution is to show that this slow dynamic is necessary to reproduce the correct rhythmicity of the thalamus at the network level. The conductance-based models are powerful tools to simulate a neuron with a great biophysical realism. However, they consist in high-dimensional non-linear differential equations that lead to time-consuming simulations. Therefore, the second part of this thesis investigates simple, qualitative modeling of neuron and network activity. This type of model, called hybrid model, is more mathematical; it captures the subthreshold dynamics of the neuron through differential equations and adds a reset rule to mimic the all-or-none nature of the spike. A hybrid model of a thalamic neuron has to be able to switch from spiking to bursting. Its robustness at the cellular level relies on its ability to mimic the slow dynamics of the calcium current without mathematical manipulation. The third contribution of the thesis is to confirm this discussion with a network level analysis. It shows that previously available simple models of thalamocortical neurons such as the well-known Izhikevich models lack the slow dynamics, hence they generate pathological behaviors while connected within a circuit. The key message is the comparison between two classes of thalamic neuron models. The first class integrates the slow dynamics of the calcium current while the second class assumes that this dynamics is fast. This work shows that the first class provides better results in terms of robustness. This demonstration is led at the cellular and network levels, for conductance-based models or reduced models. Therefore, the models belonging to this class are suitable for studies concerning the neuromodulation or the synaptic plasticity.Au début des années cinquante, Hodgkin et Huxley ont développé un modèle de l'activité électrique neuronale. Basé sur un simple circuit RC caracterisé par des conductances non-linéaires, ils ont réussi à reproduire de manière précise le comportement électrique d'un neurone de calamar. Durant ces dernières années, l'augmentation du nombre de données expérimentales disponibles, ainsi qu'une amélioration des connaissances dans le domaine de la neuroscience, ont permis aux scientifiques d' étendre le modèle de Hodgkin et Huxley au cas de neurones plus complexes. Cependant, ces modèles augmentent également en complexité mathématique, ce qui les rend moins robustes. Cette thèse se concentre uniquement sur les neurones du thalamus. Cette partie du cerveau est le centre-relais des informations sensorielles voyageant vers le cortex. En fonction de leur état, les neurones du thalamus sont caractérisés par deux motifs de décharge. Durant le sommeil, les neurones "burstent", ce qui bloque le traitement de l'information. Durant la phase d'éveil, les neurones présentent un enchaînement régulier de pics qui permettent au thalamus de traiter l'information et de l'envoyer au cortex. Un model robuste décrivant ce changement d'activité est primordial afin de mieux comprendre certaines maladies telles que l'absence d'épilepsie ou de d'écrire plus précisément le comportement du thalamus. De récentes études ont mis en évidence le rôle critique de la dynamique lente des courants calciques présents dans les neurones dans la transition entre les deux modes de décharge. Inspirée par ces recherches, cette thèse rassemble des modèles à conductances des neurones du thalamus présents dans la littérature. La différence majeure entre ces modèles réside dans l'intégration ou non de la cinétique lente des courants calciques. La première contribution de ce travail est la comparaison de leur robustesse à l'échelle cellulaire. Les modèles qui omettent cette dynamique lente sont fragiles lorsqu'ils sont soumis à des perturbations. La deuxième contribution consiste à montrer que cette caractéristique des courants calciques est nécessaire pour reproduire le rythme d'une population de neurones du thalamus. Les modèles à conductances sont des outils puissants pour simuler un neurone avec une bonne interpretation biophysique. Cependant, ils sont formés d'un grand nombre d'équations différentielles non-linéaires menant à des simulations couteuses en temps. Par conséquent, la deuxième partie de cette thèse s'oriente vers une modélisation plus simple et plus qualitative des neurones et de leur activité en réseau. Ce type de modèle, appelé modèle hybride, est plus mathématique ; il capture la dynamique du signal neuronal au travers une équation différentielle. Ensuite, une équation de remise à zéro, appelée la règle du "reset", tient compte de la nature "tout ou rien" des pics présents dans le signal électrique. Un modèle hybride d'un neurone du thalamus doit être capable de reproduire la transition entre les deux modes de décharges. Sa robustesse à l'échelle cellulaire repose sur son aptitude à imiter la dynamique lente des courants calciques sans manipulation mathématique. La troisième contribution de cette thèse est de confirmer cette hypothèse avec une analyse à l'échelle d'un réseau de neurones. Cette étude prouve que les modèles plus simples des neurones du thalamus présents dans la littérature, tels que les modèles d'Izhikevich, n'intègrent pas cette cinétique lente. Par conséquent, ils ne sont pas capables de reproduire l'activité rythmique du thalamus. Pour résumer, cette thèse a pour but de comparer deux classes de modèles de neurones du thalamus. Une classe intègre la dynamique lente des courants calciques en opposition à l'autre classe qui assume que cette dynamique est rapide. Ce travail montre que la classe faisant l'hypothèse d'une dynamique lente donne des résultats favorables en terme de robustesse. Cette démonstration est menée au niveau cellulaire et à l'échelle d'un réseau de neurones, tant pour des modèles à conductances que des modèles réduits

    Interactions between synaptic plasticity and switches in brain states for memory consolidation: a modeling study

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    Once a day, every individual lay down and becomes unconscious. Isn’t sleep a strange thing to do? Despite the risks associated with it, our ancestors used to sleep too, suggesting that it should provide an evolutionary advantage. Thus, it raises a fundamental question: why do we sleep? Among all essential functions of sleep, research has proved its preponderant role in memory formation and consolidation. At the cellular level, memory is achieved through processes referred to as synaptic plasticity and translating the remarkable ability of the brain to constantly evolve due to various stimuli. Furthermore, differences in the neuronal firing patterns have been highlighted between wake and sleep: during sleep, neurons are bursting while during wake, neurons show a tonic firing pattern. Memory is an abstract concept, it is not a simple task to understand the processes behind it. As experimental evidence provides insights about how plasticity is induced, modeling techniques reproducing experimental data can give insights about memory mechanisms. Literature is broad concerning plasticity modeling. In this work, a concise review of phenomenological models is conducted. Then, some of them are implemented in a conductance-based model able to switch from waking to sleep, i.e. from tonic to bursting activity. Compared to simplified spiking neuron model, this conductance-based model is a powerful tool to be able to faithfully replicate neuronal behavior in waking and sleeping period. Reproduction of experimental protocols is carried in tonic mode as well as the impact of variability in the firing pattern to mimic more in vivo situations. As the ultimate goal of this thesis is to see the impact of existing models on memory consolidation during sleep, their robustness and behaviour during a bursting period are investigated. It led to unsatisfactory results regarding memory consolidation, highlighting the limitations of those phenomenological models. The behaviour of the models implemented highly depends on the method used to bound the synaptic weight in-between extreme values. Finally, insights about neuromodulation are suggested as improvements

    Modélisation de la consolidation de la mémoire dépendante de l'état d'activité du cerveau

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    Our brains enable us to perform complex actions and respond quickly to the external world, thanks to transitions between different brain states that reflect the activity of interconnected neuronal populations. An intriguing example is the ever-present switch of brain activity that occurs while transitioning between periods of active and quiet waking. It involves transitions from small-amplitude, high-frequency brain oscillations to large-amplitude, low-frequency oscillations, accompanied by neuronal activity switches from tonic firing to bursting. The switch between these firing modes is regulated by neuromodulators and the inherent properties of neurons. Simultaneously, our brains have the ability to learn and form memories through persistent changes in the strength of the connections between neurons. This process is known as synaptic plasticity, where neurons strengthen or weaken connections based on their respective firing activity. While it is commonly believed that putting in more effort and time leads to better performance when memorizing new information, this thesis explores the hypothesis that taking occasional breaks and allowing the brain to rest during quiet waking periods may actually be beneficial. Using a computational approach, the thesis investigates the relationship between the transitions in brain states from active to quiet waking described by the neuronal switches from tonic firing to bursting, and synaptic plasticity on memory consolidation. To investigate this research question, we constructed neurons and circuits with the ability to switch between tonic firing and bursting using a conductance-based approach. In our first contribution, we focused on identifying the key neuronal property that enables robust switches, even in the presence of neuron and circuit heterogeneity. Through computational experiments and phase plane analysis, we demonstrated the significance of a distinct timescale separation between sodium and T-type calcium channel activation by comparing various models from the existing literature. Synaptic plasticity is studied to understand learning and memory consolidation. The second contribution involves a taxonomy of synaptic plasticity rules, investigating their compatibility with switches in neuronal activity, small neuronal variabilities, and neuromodulators. The third contribution reveals the evolution of synaptic weights during the transition from tonic firing in active waking to bursting in quiet waking. Combining bursting neurons with traditional synaptic plasticity rules using soft-bounds leads to a homeostatic reset, where synaptic weights converge to a fixed point regardless of the weights acquired during tonic firing. Strong weights depress, while weak weights potentiate until reaching a set point. This homeostatic mechanism is robust to neuron and circuit heterogeneity and the choice of synaptic plasticity rules. The reset is further exploited by neuromodulator-induced changes in synaptic rules, potentially supporting the Synaptic-Tagging and Capture hypothesis, where strong weights are tagged and converge to a high reset value during bursting. While burst-induced reset may cause forgetting of previous learning, it also restores synaptic weights and facilitates the formation of new memories. To exploit this homeostatic property, an innovative burst-dependent structural plasticity rule is developed to encode previous learning through long-lasting morphological changes. The proposed mechanism explains late-stage of Long-Term Potentiation, complementing traditional synaptic plasticity rules governing early-stage of Long-Term Potentiation. Switches to bursting enable neurons to consolidate synapses by creating new proteins and promoting synapse growth, while simultaneously restoring efficacy of postsynaptic receptors for new learning. The novel plasticity rule is validated by comparing it with traditional synaptic rules in various memory tasks. The results demonstrate that switches from tonic firing to bursting and the novel structural plasticity enhance learning and memory consolidation. In conclusion, this thesis utilizes computational models of biophysical neurons to provide evidence that the switches from tonic firing to bursting, reflecting the shift from active to quiet waking, play a crucial role in enhancing memory consolidation through structural plasticity. In essence, this thesis offers computational support for the significance of taking breaks and allowing our brains to rest in order to solidify our memories. These findings serve as motivation for collaborative experiments between computational and experimental neuroscience, fostering a deeper understanding of the biological mechanisms underlying brain-state-dependent memory consolidation. Furthermore, these insights have the potential to inspire advancements in machine learning algorithms by incorporating principles of neuronal activity switches
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