The research discussed here examines how normal healthy aging affects spatial suppressive mechanisms in a variety of visual tasks using both static and dynamic stimuli. Prior research has suggested that younger adults demonstrate a center-surround antagonistic pattern in which they show spatial summation at low contrast and spatial suppression at high contrast in brief motion direction discrimination tasks. Older adults have been shown to have reduced spatial suppression at high contrast and this is thought to be related to reduced GABAergic inhibition in the visual cortex. The results obtained from this program of research suggest that age-related changes in optical and neural visual mechanisms do not affect spatiotemporal mechanisms for static stimuli when the target is presented with the mask (embedded masking). However, when the mask appears immediately before (forward masking) or after (backward masking) the target, older adults require more contrast to detect the target (Chapter 2). In addition, spatial suppression is not reduced for older adults in a task with moving stimuli presented at long durations, even with increasing speed (Chapter 3). In Chapter 4, we used static stimuli presented at brief durations to induce a sudden motion onset and found that although there was no significant age difference in spatial suppression, there was a trend showing reduced levels of spatial suppression in older adults. These results taken together suggest that inhibitory neural mechanisms in the visual cortex may mediate spatial suppression for briefly presented stimuli only. / Thesis / Doctor of Philosophy (PhD)

Regulation of metabolism is vital to health and lies at the core of many different diseases. The breakdown of metabolisms' regulation within the brain can lead to neurological disease like Alzheimer's Disease (AD). AD is known to affect brain regions responsible for memory and memory processing like the hippocampus and entorhinal cortex. The regulation of these regions' protein quality, synthesis, and degradation deviate from 'normal' or 'healthy' levels when AD is happening. It is known there is a breakdown of regulation in those regions; however, little is known about the specifics of regulation in healthy brains regions or how it changes with disease. Using the sample collection method of microsampling in combination with kinetic proteomics we investigated proteostasis control in regions known to be affected by AD relative to a control region. This provides a baseline for proteins and ontologies found in the proteomes under healthy circumstances. The regions are all the same tissue type; however, since different regions of the brain perform different functions, the metabolism and therefore the regulation of proteostasis are different. By understanding how regional brain proteomes are regulated in young healthy mice, we are prepared for comparisons against diseased tissue in future work.

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No / The right temporoparietal cortex plays a critical role in body representation. Here, we applied repetitive transcranial magnetic stimulation (rTMS) over right extrastriate body area (EBA) and temporoparietal junction (TPJ) to investigate their causative roles in perceptual representations of one's own and others' body. Healthy women adjusted size-distorted pictures of their own body or of the body of another person according to how they perceived the body (subjective task) or how others perceived it (intersubjective task). In keeping with previous reports, at baseline, we found an overall underestimation of body size. Crucially, EBA-rTMS increased the underestimation bias when participants adjusted the images according to how others perceived their own or the other woman's body, suggesting a specific role of EBA in allocentric body representations. Conversely, TPJ-rTMS increased the underestimation bias when participants adjusted the body of another person, either a familiar other or a close friend, in both subjective and intersubjective tasks, suggesting an involvement of TPJ in representing others' bodies. These effects were body-specific, since no TMS-induced modulation was observed when participants judged a familiar object. The results suggest that right EBA and TPJ play active and complementary roles in the complex interaction between the perceptions of one's own and other people's body.

Il a récemment été largement reconnu que la dynamique interne des réseaux de neurones pourraient jouer un rôle essentiel dans leur fonction. À cet régard, le "bruit synaptique" -- qui représente l'influence du réseau cortical sur les neurones individuels, et qui est une conséquence directe de la circuiterie récurrente massive du néocortex -- a récemment été identifié comme un facteur important qui affecte les propriétés intégratives des neurones. Cette activité affecte aussi l'évolution des réponses neuronales en fonction des changements d'états du cerveau, parfois en quelques secondes. Ces états d'activité générés en interne, qui résultent -- et eux-même influencent -- la plasticité des connexions synaptiques récurrentes, se combinent alors avec les entrées externes pour produire un riche répertoire de réponses aux stimuli sensoriels. Dans cette thèse, nous nous sommes concentrés sur le aspect spatial de ces dynamiques intrinsèques, en particulier la structure spatiale des oscillations corticales, à la fois dans le cas spontané et des réponses évoquées. Nous avons fait un examen approfondi de la littérature concernant la propagation d'ondes dans le thalamus et le cortex, et nous avons proposé un modèle de réseau neuronal pour examiner l'interaction entre les ondes de propagation et l'activité interne du réseau. Nous avons aussi mis en place de nouveaux outils pour la caractérisation de ce type d'activité spatio-temporelle à partir d'enregistrements multicanaux bruités. Le point culminant de ce travail est une démonstration, en utilisant les données d'imagerie par colorants voltage-sensitifs (VSD, "voltage-sensitive dye imaging") obtenues chez le singe éveillé, que la réponse de la population à un stimulus visuel se propage comme une onde sur une grande étendue du cortex visuel primaire. Ce résultat contredit une série d'études précédentes qui semblaient suggérer l'absence d'onde de propagation dans ce cas. Ensuite, nous avons commencé à étudier la structure spatio-temporelle du potentiel de champ local (``local field potential'') obtenu à partir d'enregistrements multi-électrodes chez l'homme et le singe, dans divers états cérébraux, pour répondre aux questions suscitées par l'étude initale en imagerie VSD chez le singe. En parallèle, nous avons étudié les caractéristiques de la structure de connectivité de plusieurs systèmes nerveux, en utilisant la théorie des graphes, pour identifier les aspects aléatoires ou structurés ("small-world") de cette connectivité. Le résultat principal est que, contrairement au consensus, la structure de connectivité est beaucoup plus proche d'une connectivité aléatoire. Les résultats de ces études de doctorat couvrent ainsi un grand spectre d'échelles en neurosciences, de modèles d'activité macroscopiques à des profils de connectivité microscopiques. J'espère sincèrement pouvoir exposer dans ces pages ces résultats de façon unifiée, dans le but de constituer une base pour la poursuite de ces travaux en neurosciences - une recherche de structure au sein de l'architecture interne du système nerveux central. / It has only recently been acknowledged to what large extent the internal dynamics of neural networks could play a role in their function. In this respect, synaptic "noise" -- that is, the influence of the cortical network on single neurons exerted through the massive recurrent circuity that is the hallmark of neocortex -- has recently been shown to have a profound effect on neuronal integrative properties, changing the responses of single neurons across brain states, sometimes within the matter of a few seconds. These internally generated activity states, shaped by and continually shaping the plastic synaptic recurrent connections, then combine with the external inputs to produce a rich repertoire of responses to sensory stimuli in primary cortical regions. In this thesis, we have focused on the {\it spatial} aspect of these internal dynamics, specifically the spatial structure of cortical oscillations, spontaneous and stimulus-evoked. Along the way, we have made an extensive review of the literature concerning propagating waves in thalamus and cortex, and studied network models to investigate how waves depend on network state. We have also introduced new tools for the characterization of spatiotemporal activity patterns in noisy multichannel data. The culmination of this work is a demonstration, using voltage-sensitive dye imaging data taken from the awake monkey, that the population response to a small visual stimulus propagates like a wave across a large extent of primary visual cortex during the awake state, a result contradicting a range of previous studies which seemed to suggest that propagating waves disappear in this case. Moving forward, we have begun to investigate the spatiotemporal structure of local field potential and spiking activity in multielectrode recordings taken from the human and monkey in various states of arousal, to address questions prompted by our initial voltage-sensitive dye imaging study in the monkey. In parallel, we have initiated an analysis of the extent to which neural connectivity can be characterized by the "small-world" effect, the main result of which is that neural graphs may in fact reside outside the small-world regime. The results from these PhD studies thus span the spectrum of scales in neuroscience, from macroscopic activity patterns to microscopic connectivity profiles. It is my sincere hope to expound in these pages a unified theme for these results, and a foundation for further work in neuroscience -- a search for structure within the internal architecture of the system under study.

How do circuits in the mammalian cerebral cortex encode properties of the sensory environment in a way that can drive adaptive behavior? This question is fundamental to neuroscience, but it has been very difficult to approach directly. Various computational and theoretical models can explain a wide range of phenomena observed in the primary visual cortex (V1), including the anatomical organization of its circuits, the development of functional properties like orientation tuning, and behavioral effects like surround modulation. However, so far no model has been able to bridge these levels of description to explain how the machinery that develops directly affects behavior. Bridging these levels is important, because phenomena at any one specific level can have many possible explanations, but there are far fewer possibilities to consider once all of the available evidence is taken into account. In this thesis we integrate the information gleaned about cortical development, circuit and cell-type specific interactions, and anatomical, behavioral and electrophysiological measurements, to develop a computational model of V1 that is constrained enough to make predictions across multiple levels of description. Through a series of models incorporating increasing levels of biophysical detail and becoming increasingly better constrained, we are able to make detailed predictions for the types of mechanistic interactions required for robust development of cortical maps that have a realistic anatomical organization, and thereby gain insight into the computations performed by the primary visual cortex. The initial models focus on how existing anatomical and electrophysiological knowledge can be integrated into previously abstract models to give a well-grounded and highly constrained account of the emergence of pattern-specific tuning in the primary visual cortex. More detailed models then address the interactions between specific excitatory and inhibitory cell classes in V1, and what role each cell type may play during development and function. Finally, we demonstrate how these cell classes come together to form a circuit that gives rise not only to robust development but also the development of realistic lateral connectivity patterns. Crucially, these patterns reflect the statistics of the visual environment to which the model was exposed during development. This property allows us to explore how the model is able to capture higher-order information about the environment and use that information to optimize neural coding and aid the processing of complex visual tasks. Using this model we can make a number of very specific predictions about the mechanistic workings of the brain. Specifically, the model predicts a crucial role of parvalbumin-expressing interneurons in robust development and divisive normalization, while it implicates somatostatin immunoreactive neurons in mediating longer range and feature-selective suppression. The model also makes predictions about the role of these cell classes in efficient neural coding and under what conditions the model fails to organize. In particular, we show that a tight coupling of activity between the principal excitatory population and the parvalbumin population is central to robust and stable responses and organization, which may have implications for a variety of diseases where parvalbumin interneuron function is impaired, such as schizophrenia and autism. Further the model explains the switch from facilitatory to suppressive surround modulation effects as a simple by-product of the facilitating response function of long-range excitatory connections targeting a specialized class of inhibitory interneurons. Finally, the model allows us to make predictions about the statistics that are encoded in the extensive network of long-range intra-areal connectivity in V1, suggesting that even V1 can capture high-level statistical dependencies in the visual environment. The final model represents a comprehensive and well constrained model of the primary visual cortex, which for the first time can relate the physiological properties of individual cell classes to their role in development, learning and function. While the model is specifically tuned for V1, all mechanisms introduced are completely general, and can be used as a general cortical model, useful for studying phenomena across the visual cortex and even the cortex as a whole. This work is also highly relevant for clinical neuroscience, as the cell types studied here have been implicated in neurological disorders as wide ranging as autism, schizophrenia and Parkinson’s disease.

A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e comportamento em rajada, o que pode acarretar em diferentes papéis que os neurônios podem ter na rede. Nesta dissertação é feita uma análise sob o ponto de vista de sistemas dinâmicos e de análise de sensibilidade de seis modelos ao estilo de Hodgkin-Huxley e compartimentais de neurônios encontrados no córtex visual primário de mamíferos. Esses modelos correspondem a seis classes eletrofisiológicas de neurônios corticais e o estudo feito nesta dissertação oferece uma contribuição ao entendimento dos princípios de sistemas dinâmicos subjacentes a essa classificação. / Computational neuroscience is a vast scientific area which has as subject of study the unsderstanding or emulation of brain dynamics at different levels. This work studies the dynamics of neurons, which are believed, according to present consensus, to be the fundamental processing units of the brain. The importance of studying neuronal behavior comes from the diversity of properties they may have. This study becomes richer when there are interactions between distintic neuronal internal systems, in different time scales, creating properties like adaptation, latency and bursting, resulting in different roles that neurons may have in the network. This dissertation contains a study of six reduced compartmental conductance-based models of neurons found in the primary visual cortex of mammals under the dynamical systems and sensitivity analysis viewpoints. These models correspond to six eletrophysiological classes of cortical neurons and this dissertation offers a contribution to the understanding of the dynamical-systems principles underlying such classification.

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