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Despite its great complexity, a great deal is known about the organization and information-processing properties of the visual system. However, the neural correlates of visual awareness are not yet understood. By studying patients with blindsight, the primary visual cortex (V1) has attracted a lot of attention recently. Although this brain area appears to be important for visual awareness, its exact role is still a matter of debate. Interactive models propose a direct role for V1 in generating visual awareness through recurrent processing. Hierarchal models instead propose that awareness is generated in later visual areas and that the role of V1 is limited to transmitting the necessary information to these areas. Interactive and hierarchical models make different predictions and the aim of this thesis is to review the evidence from lesions, perceptual suppression, and transcranial magnetic stimulation (TMS), along with data from internally generated visual awareness in dreams, hallucinations and imagery, this in order to see whether current evidence favor one type of model over the other. A review of the evidence suggests that feedback projections to V1 appear to be important in most cases for visual awareness to arise but it can arise even when V1 is absent.

Motion in the outside world forms one of the primary uses of visual information for many animals. The ability to interpret motion quickly and accurately permits interaction with and response to events in the outside world. While much is known about some aspects of motion perception, there is less agreement about how feature selectivity leading to motion perception is actually formed in the convergent and divergent pathways of the visual system. It is even less clear how these classical understandings of motion processing, often driven by artificial stimuli with little resemblance to the outside world, correspond to responses of neurons when using more natural stimuli. In this thesis, we probe these gaps, first by demonstrating that synchronization within the visual thalamus leads to efficient representations of motion (through tuning properties) in primary visual cortex, exploiting precise timing across populations in a unique manner compared to traditional models. We then create a novel “minimally-natural” stimulus with the appearance of an infinite hallway wallpapered with sinusoidal gratings, to probe how such minimally natural features modulate our predictions of neural responses based upon feature tuning properties. Through encoding and decoding models we find that measuring a restricted tuning parameter space limits our ability to capture all response properties but preserves relevant information for decoding. We finish with an exploration of ethologically relevant natural features, perspective and complex motion, and show that even moderate amounts of each feature within or near the classical V1 receptive field changes the neural response from what classical feature tuning would predict and improves stimulus classification tremendously. Together all of these results indicate that capturing information about motion in the outside world through visual stimuli requires a more advanced model of feature selectivity that incorporates parameters based on more complex spatial relationships.

Our ability to learn relies on the potential of neuronal networks to change through experience. The primary visual cortex (V1) has become a popular system for studying how experience shapes cortical neuronal networks. Experience-dependent plasticity in V1 has been extensively studied in young animals, revealing that experiences in early postnatal life substantially shape neuronal activity in the developing cortex. In contrast, less is known about how experiences modify the representation of visual stimuli in the adult brain. In addition, adult experience-dependent plasticity remains largely unexplored in neurodevelopmental disorders. To address this issue, we established a two-photon calcium imaging set-up, suitable for chronic imaging of neuronal activity in awake-behaving mice. We implemented protocols for the reliable expression of genetically encoded calcium indicators (GCaMP6), for the implantation of a chronic cranial window and for the analysis of chronic calcium imaging data. This approach enables us to monitor the activity of hundreds of neurons across days, and up to 4-5 weeks. We used this technique to determine whether the daily exposure to high-contrast gratings would induce experience-dependent changes in V1 neuronal activity. We monitored the activity of putative excitatory neurons and of three non-overlapping populations of inhibitory interneurons in layer 2/3 of adult mice freely running on a cylindrical treadmill. We compared the results obtained from mice that were exposed daily to either a high-contrast grating or to a grey screen and characterized their neuronal response properties. Our results did not reveal significant differences in neuronal properties between these two groups, suggesting a lack of stimulus-specific plasticity in our experimental conditions. However, we did observe and characterize, in both groups, a wide range of activity changes in individual cells over time. We finally applied the same method to investigate impairments in experience-dependent plasticity in a mouse model of intellectual disability (ID), caused by synaptic GTPase-activating protein (SynGAP) haploinsufficiency. SynGAP haploinsufficiency is a common de novo genetic cause of non-syndromic ID and is considered a Type1 risk for autism spectrum disorders. While the impact of Syngap gene mutations has been thoroughly studied at the molecular and cellular levels, neuronal network deficits in vivo remain largely unexplored. In this study, we compared in vivo neuronal activity before and after monocular deprivation in adult mutant mice and littermate controls. These results revealed differences in baseline network activity between both experimental groups. These impairments in cortical neuronal network activity may underlie sensory and cognitive deficits in patients with Syngap gene mutations.

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.

The primary visual cortex (V1) has long been considered the main low level visual analysis area of the brain. The classical view is of a feedfoward system functioning as an edge detector, in which each cell has a receptive field (RF) and a preferred orientation. Whilst intuitive, this view is not the whole story. Although stimuli outside a neuron’s RF do not result in an increased response by themselves, they do modulate a neuron’s response to what’s inside its RF. We will refer to such extra-RF effects as contextual modulation. Contextual modulation is thought to underlie several perceptual phenomena, such as various orientation illusions and saliency of specific features (such as a contour or differing element). This gives a view of V1 as more than a collection of edge detectors, with neurons collectively extracting information beyond their RFs. However, many of the accounts linking psychophysics and physiology explain only a small subset of the illusions and saliency effects: we would like to find a common principle. So first, we assume the contextual modulations experienced by V1 neurons is determined by the elastica model, which describes the shape of the smoothest curve between two points. This single assumption gives rise to a wide range of known contextual modulation and psychophysical effects. Next, we consider the more general problem of encoding and decoding multi-variate stimuli (such as center surround gratings) in neurons, and how well the stimuli can be decoded under substantial noise levels with a maximum likelihood decoder. Although the maximum likelihood decoder is widely considered optimal and unbiased in the limit of no noise, under higher noise levels it is poorly understood. We show how higher noise levels lead to highly complex decoding distributions even for simple encoding models, which provides several psychophysical predictions. We next incorporate more updated experimental knowledge of contextual modulations. Perhaps the most common form of contextual modulations is center surround modulation. Here, the response to a center grating in the RF is modulated by the presence of a surrounding grating (the surround). Classically this modulation is considered strongest when the surround is aligned with the preferred orientation, but several studies have shown how many neurons instead experience strongest modulation whenever center and surround are aligned. We show how the latter type of modulation gives rise to stronger saliency effects and unbiased encoding of the center. Finally, we take an experimental perspective. Recently, both the presence and the underlying mechanisms of contextual modulations has been increasingly studied in mice using calcium imaging. However, cell signals extracted with calcium imaging are often highly contaminated by other sources. As contextual effects beyond center surround modulation can be subtle, a method is needed to remove the contamination. We present an analysis toolbox to de-contaminate calcium signals with blind source separation. This thesis thus expands our understanding of contextual modulation, predicts several new experimental results, and presents a toolbox to extract signals from calcium imaging data which should allow for more in depth studies of contextual modulation.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-08-18T12:54:46Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Antonio Jorge Fontenele Neto.pdf: 9942923 bytes, checksum: 9de5bf466a9fc72acbc6a2a2d3a9c57c (MD5) / Made available in DSpace on 2016-08-18T12:54:46Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Antonio Jorge Fontenele Neto.pdf: 9942923 bytes, checksum: 9de5bf466a9fc72acbc6a2a2d3a9c57c (MD5) Previous issue date: 2015-08-25 / CAPEs / O córtex visual primário (V1) é a região do córtex cerebral responsável pela primeira etapa de processamento da informação visual capturada pela retina. Por ser uma das áreas corticais melhor compreendidas, V1 constitui um dos principais paradigmas de compreensão do processamento sensorial. Desde os anos 70 há uma extensa literatura que estuda propriedades de resposta de neurônios de V1, principalmente com eletrodos individuais e utilizando-se como modelo animal gatos e macacos. Tem-se conhecimento de onde partem seus principais inputs e quais estímulos fazem os neurônios dispararem (grades senoidais com determinadas frequências espaciais e temporais). Mais recentemente, com o uso de matrizes de eletrodos, se tornou possível a investigação de propriedades coletivas da atividade e codificação neurais, que não eram possíveis de serem desvendadas com eletrodos individuais. Além disso, no estado da arte tecnológico atual, o uso do rato como modelo animal permite o registro da atividade neural com os animais em comportamento livre (sem anestesia ou contenção). No entanto, pouco se sabe sobre especificidades das propriedades de resposta dos neurônios do córtex visual do rato. Este trabalho teve por objetivo desenvolver um aparato e um protocolo experimental no Laboratório de Neurociência de Sistemas e Computacional adequado para estudo das propriedades de resposta de neurônios de V1 de ratos usando matrizes de eletrodos. Finalmente, apresentamos resultados experimentais onde caracterizamos respostas de neurônios de V1 a diferentes estímulos visuais (Funções de Gabor ou Grades) seja em ruído denso ou rarefeito, variando as propriedades de frequências temporal e espacial de estimulação, densidades de estímulos, velocidade, etc. Concluímos que implementamos com sucesso a técnica experimental, que abre inúmeras perspectivas futuras de pesquisas nesta linha no Departamento de Física da Universidade Federal de Pernambuco. / The primary visual cortex (V1) is the cerebral cortex region responsible for the first processing step of the visual information captured by the retina. Being one of the most studied and well described cortical sensory areas, V1 is one of the main paradigms for the study of sensory processing. Since the 70s, there is a vast literature that studies properties of V1’s neurons, specially using single electrodes and using cats and monkeys as animal models. The anatomical conectivity of the visual pathway is known, from the retina to the lateral geniculate nucleus to V1, as well as the main visual stimulations that make V1 neurons fire (sinusoidal gratings with certain spatial and temporal frequencies). More recently, using multielectrode arrays, it became possible to study coletive properties of the activity and neural codification, that could not be unveiled with single electrodes. Furthermore with, the current state of the art in multielectrode recordings it is possible to record the neural activity in frelly behaving rats (without anesthesia or restraint). This represents an advantage in using the rat as animal model. However, little is known about specificities of the V1 neurons response properties in the rat. The aim of this work is to develop, in the Laboratório de Neurociência de Sistemas e Computacional, an apparatus and an experimental protocol suitable for the study of visual response properties of V1’s neurons in rats, using multielectrode array recordings. Finally, we present experimental results that characterize the response of V1’s neurons with different visual stimuli (Gabor or Grating Functions), either in dense os sparse noise modes, varying the spatial and temporal stimulation frequencies, stimulus density, speed, etc. We conclude that the experimental technique was implemented successfully. These results open important perspectives of future research on this field for the Departamento de Física at the Universidade Federal de Pernambuco.

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.