No description available.

The hippocampal CA1 is crucial for myriad types of learning and memory. It is theorized to provide a spatiotemporal framework for the encoding of relevant information during learning, allowing an individual to create a cognitive map of its environment and experiences. To probe CA1 network dynamics that underlie such complex cognitive function, in this work we used recently developed cellular optical imaging techniques that provide high spatial and temporal resolutions. Genetically-encoded calcium indicators offer the ability to record intracellular calcium dynamics, a proxy of neural activity, from hundreds of cells in behaving animals with single cell resolution in genetically-defined cell types. In complement, recently developed genetically-encoded voltage indicators have enabled direct recording of transmembrane voltage of individual genetically-defined cells in behaving animals. The work presented here uses the genetically-encoded calcium indicator GCaMP6f and the genetically-encoded voltage indicator SomArchon to interrogate the activities of individual hippocampal CA1 neurons and their relationship to the dynamics of the broader network during behavior. First, we provide the first in vivo, real-time evidence that two unique populations of CA1 cells encode trace conditioning and extinction learning, two distinct phases of hippocampal-dependent learning. The population of cells responsible for the representation of extinction learning emerges within one session of extinction training. Second, we perform calcium imaging in a mouse model containing a total knockout of NEXMIF, a gene causative of autism spectrum disorder. We reveal that loss of NEXMIF causes over-synchronization of the CA1 circuit, particularly during locomotion, impairing the information encoding capacity of the network. Finally, we conduct voltage imaging of CA1 pyramidal cells and parvalbumin (PV)-positive interneurons, with simultaneous recording of local field potential (LFP), to characterize how cellular-level membrane dynamics and spiking relate to network-level LFP. We demonstrate that in PV neurons, membrane potential oscillations in the theta frequency range show consistent synchrony with LFP theta oscillations and organize spike timing of the PV population relative to LFP theta, indicating that PV interneurons orchestrate theta rhythmicity in the CA1 network. In summary, this dissertation utilizes genetically-encoded optical reporters of neural activity, providing critical insights into the function of the CA1 as a flexible, diverse network of individual neurons.

Organisms can form safety associations with cues that predict the absence of an aversive event. This cognitive process, learned safety, is important for modulating emotional processing, as safety cues can decrease fear in the presence of previously learned danger cues. Further, there are clinical implications in understanding learned safety, as individuals with PTSD present with deficits in learned safety. Additionally, there is a well established relationship between smoking and PTSD. The link between smoking and PTSD is unclear, however one possibility is that nicotine-associated changes in cognition could facilitate PTSD symptoms, particularly by disrupting are altering learned safety. Considering that nicotine has been shown to modulate associative learning, including hippocampus-dependent forms of fear learning, we hypothesized that nicotine administration could cause maladaptive associative learning to occur, leading to altered safety learning. In the present study, mice were administered acute nicotine and trained and tested in two forms of cued safety learning, explicitly unpaired and backwards trace conditioning. To test for conditioned inhibition of fear by safety cues we performed summation testing. Summation testing indicated that acute nicotine did not impact unpaired learned safety, but did disrupt backwards trace conditioned safety. Additionally, chronic nicotine was found to have no effect on backwards trace conditioned safety, suggesting the development of tolerance. Importantly, on a separate test in which the backwards trace conditioned stimulus was presented alone in a novel context, acute nicotine administration was found to facilitate a fear association with the backwards trace conditioned stimulus. Therefore, acute nicotine prevented backwards trace conditioned safety, by facilitating the formation of a maladaptive fear association. Finally, we found that infusion of nicotine into the dorsal hippocampus and medial prefrontal cortex resulted in similar maladaptive behavioral patterns in summation testing. These findings are discussed with respect to how nicotine can alter cognition and the role alterations in cognition may play PTSD. / Psychology

Tout au long de la vie, le cerveau développe des représentations de son environnement permettant à l’individu d’en tirer meilleur profit. Comment ces représentations se développent-elles pendant la quête de récompenses demeure un mystère. Il est raisonnable de penser que le cortex est le siège de ces représentations et que les ganglions de la base jouent un rôle important dans la maximisation des récompenses. En particulier, les neurones dopaminergiques semblent coder un signal d’erreur de prédiction de récompense. Cette thèse étudie le problème en construisant, à l’aide de l’apprentissage machine, un modèle informatique intégrant de nombreuses évidences neurologiques. Après une introduction au cadre mathématique et à quelques algorithmes de l’apprentissage machine, un survol de l’apprentissage en psychologie et en neuroscience et une revue des modèles de l’apprentissage dans les ganglions de la base, la thèse comporte trois articles. Le premier montre qu’il est possible d’apprendre à maximiser ses récompenses tout en développant de meilleures représentations des entrées. Le second article porte sur l'important problème toujours non résolu de la représentation du temps. Il démontre qu’une représentation du temps peut être acquise automatiquement dans un réseau de neurones artificiels faisant office de mémoire de travail. La représentation développée par le modèle ressemble beaucoup à l’activité de neurones corticaux dans des tâches similaires. De plus, le modèle montre que l’utilisation du signal d’erreur de récompense peut accélérer la construction de ces représentations temporelles. Finalement, il montre qu’une telle représentation acquise automatiquement dans le cortex peut fournir l’information nécessaire aux ganglions de la base pour expliquer le signal dopaminergique. Enfin, le troisième article évalue le pouvoir explicatif et prédictif du modèle sur différentes situations comme la présence ou l’absence d’un stimulus (conditionnement classique ou de trace) pendant l’attente de la récompense. En plus de faire des prédictions très intéressantes en lien avec la littérature sur les intervalles de temps, l’article révèle certaines lacunes du modèle qui devront être améliorées. Bref, cette thèse étend les modèles actuels de l’apprentissage des ganglions de la base et du système dopaminergique au développement concurrent de représentations temporelles dans le cortex et aux interactions de ces deux structures. / Throughout lifetime, the brain develops abstract representations of its environment that allow the individual to maximize his benefits. How these representations are developed while trying to acquire rewards remains a mystery. It is reasonable to assume that these representations arise in the cortex and that the basal ganglia are playing an important role in reward maximization. In particular, dopaminergic neurons appear to code a reward prediction error signal. This thesis studies the problem by constructing, using machine learning tools, a computational model that incorporates a number of relevant neurophysiological findings. After an introduction to the machine learning framework and to some of its algorithms, an overview of learning in psychology and neuroscience, and a review of models of learning in the basal ganglia, the thesis comprises three papers. The first article shows that it is possible to learn a better representation of the inputs while learning to maximize reward. The second paper addresses the important and still unresolved problem of the representation of time in the brain. The paper shows that a time representation can be acquired automatically in an artificial neural network acting like a working memory. The representation learned by the model closely resembles the activity of cortical neurons in similar tasks. Moreover, the model shows that the reward prediction error signal could accelerate the development of the temporal representation. Finally, it shows that if such a learned representation exists in the cortex, it could provide the necessary information to the basal ganglia to explain the dopaminergic signal. The third article evaluates the explanatory and predictive power of the model on the effects of differences in task conditions such as the presence or absence of a stimulus (classical versus trace conditioning) while waiting for the reward. Beyond making interesting predictions relevant to the timing literature, the paper reveals some shortcomings of the model that will need to be resolved. In summary, this thesis extends current models of reinforcement learning of the basal ganglia and the dopaminergic system to the concurrent development of representation in the cortex and to the interactions between these two regions.

Tout au long de la vie, le cerveau développe des représentations de son environnement permettant à l’individu d’en tirer meilleur profit. Comment ces représentations se développent-elles pendant la quête de récompenses demeure un mystère. Il est raisonnable de penser que le cortex est le siège de ces représentations et que les ganglions de la base jouent un rôle important dans la maximisation des récompenses. En particulier, les neurones dopaminergiques semblent coder un signal d’erreur de prédiction de récompense. Cette thèse étudie le problème en construisant, à l’aide de l’apprentissage machine, un modèle informatique intégrant de nombreuses évidences neurologiques. Après une introduction au cadre mathématique et à quelques algorithmes de l’apprentissage machine, un survol de l’apprentissage en psychologie et en neuroscience et une revue des modèles de l’apprentissage dans les ganglions de la base, la thèse comporte trois articles. Le premier montre qu’il est possible d’apprendre à maximiser ses récompenses tout en développant de meilleures représentations des entrées. Le second article porte sur l'important problème toujours non résolu de la représentation du temps. Il démontre qu’une représentation du temps peut être acquise automatiquement dans un réseau de neurones artificiels faisant office de mémoire de travail. La représentation développée par le modèle ressemble beaucoup à l’activité de neurones corticaux dans des tâches similaires. De plus, le modèle montre que l’utilisation du signal d’erreur de récompense peut accélérer la construction de ces représentations temporelles. Finalement, il montre qu’une telle représentation acquise automatiquement dans le cortex peut fournir l’information nécessaire aux ganglions de la base pour expliquer le signal dopaminergique. Enfin, le troisième article évalue le pouvoir explicatif et prédictif du modèle sur différentes situations comme la présence ou l’absence d’un stimulus (conditionnement classique ou de trace) pendant l’attente de la récompense. En plus de faire des prédictions très intéressantes en lien avec la littérature sur les intervalles de temps, l’article révèle certaines lacunes du modèle qui devront être améliorées. Bref, cette thèse étend les modèles actuels de l’apprentissage des ganglions de la base et du système dopaminergique au développement concurrent de représentations temporelles dans le cortex et aux interactions de ces deux structures. / Throughout lifetime, the brain develops abstract representations of its environment that allow the individual to maximize his benefits. How these representations are developed while trying to acquire rewards remains a mystery. It is reasonable to assume that these representations arise in the cortex and that the basal ganglia are playing an important role in reward maximization. In particular, dopaminergic neurons appear to code a reward prediction error signal. This thesis studies the problem by constructing, using machine learning tools, a computational model that incorporates a number of relevant neurophysiological findings. After an introduction to the machine learning framework and to some of its algorithms, an overview of learning in psychology and neuroscience, and a review of models of learning in the basal ganglia, the thesis comprises three papers. The first article shows that it is possible to learn a better representation of the inputs while learning to maximize reward. The second paper addresses the important and still unresolved problem of the representation of time in the brain. The paper shows that a time representation can be acquired automatically in an artificial neural network acting like a working memory. The representation learned by the model closely resembles the activity of cortical neurons in similar tasks. Moreover, the model shows that the reward prediction error signal could accelerate the development of the temporal representation. Finally, it shows that if such a learned representation exists in the cortex, it could provide the necessary information to the basal ganglia to explain the dopaminergic signal. The third article evaluates the explanatory and predictive power of the model on the effects of differences in task conditions such as the presence or absence of a stimulus (classical versus trace conditioning) while waiting for the reward. Beyond making interesting predictions relevant to the timing literature, the paper reveals some shortcomings of the model that will need to be resolved. In summary, this thesis extends current models of reinforcement learning of the basal ganglia and the dopaminergic system to the concurrent development of representation in the cortex and to the interactions between these two regions.