Dans le cortex visuel primaire du chat (aires 17 et 18), les neurones répondant aux orientations présentes dans l’environnement (comme le contour des objets) sont organisés en colonnes perpendiculaires à la surface du cortex. Il a précédemment été montré qu'un changement drastique des orientations présentes dans l’environnement change la réponse des neurones. Par exemple, un neurone répondant à des orientations horizontales pourra répondre, après apprentissage d'un nouvel environnement, à des orientations obliques. Nous avons voulu, dans cette thèse, suivre les changements de propriétés de populations entières de neurones suite à ce type d'apprentissage. A cet effet, nous avons utilisé la technique d'imagerie optique des signaux intrinsèques, qui permet de mesurer l'activité d'une surface de cortex en utilisant le signal BOLD (blood-oxygen-level dependent). Cette thèse s'articule sur trois axes : l'effet de l'apprentissage au niveau local, l'effet de l’apprentissage à l'échelle de l'aire cérébrale, et la modélisation de l’apprentissage. Dans la première partie, nous avons comparé les changements d’orientations des neurones en fonction du gradient d’orientation local. Ce gradient est fort quand deux neurones voisins ont des orientations très différentes, et faible quand leurs orientations sont semblables. Les résultats montrent que plus les neurones sont entourés de neurones aux orientations différentes, plus l'apprentissage change leur réponse à l’orientation. Ceci suggère que les connexions locales ont une influence déterminante sur l'ampleur de l’apprentissage. Dans la deuxième partie, nous avons comparé le changement d’orientation des neurones des aires 17 et 18 avant et après apprentissage. Les résultats ne sont pas notablement différents entre les aires 17 et 18. On peut toutefois noter que les changements d’orientations dans l’aire 18 ont des amplitudes plus variables que dans l’aire 17. Ceci peut provenir du fait que l’aire 18 reçoit des afférences plus variées que l’aire 17, notamment une afférence directe des cellules Y du CGLd (Corps Genouillé Latéral dorsal). Dans la troisième partie, nous avons modélisé l'apprentissage expérimentalement observé à l'aide de réseaux de neurones utilisant un apprentissage Hebbien (cartes auto-organisatrices). Nous avons montré que le « feedback » des aires supérieures vers le cortex visuel primaire était souhaitable pour la conservation de la sélectivité à l'orientation des neurones. De manière générale, cette thèse montre l'importance des connexions locales dans la plasticité neuronale. Notamment, elles garantissent un apprentissage homéostatique, c'est-à- dire conservant la représentativité des orientations au niveau du cortex. De manière complémentaire, elle montre également l’importance des aires supérieures dans le maintient à long terme des orientations apprises par les neurones lors de l'apprentissage. / In the cat primary visual cortex (areas 17 and 18), neurons responding to orientations in the environment (such as the outline of objects) are organized in columns perpendicular to the cortical surface. It was previously shown that a drastic change in orientations in the environment changes the response of neurons. For example, a neuron responding to a horizontal orientation will respond, after learning a new environment, to an oblique orientation. In this thesis, we seek to follow the changes of properties of large populations of neurons due to this type of learning. To this end, we used the intrinsic signals optical imaging technique, which measures the activity of a cortical surface using the BOLD (blood-oxygen-level dependent) signal. This thesis follows three axes: the effect of learning at the local level, the effect of learning at the visual area scale, and the modeling of learning. In the first part, we compared the changes in orientation of neurons according to the local gradient of orientation. This gradient is strong when two neighboring neurons have very different orientations, and weak when their orientations are similar. The obtained relation between the gradient and the magnitude of change in orientation shows that when neurons are increasingly surrounded by neurons with different orientations, they change their response to orientation to a greater extent. This suggests that local connections have a decisive influence on the extent of learning. In the second part, we followed the change in the orientation of neurons in the areas 17 and 18, before and after learning. The results are not significantly different between area 17 and area 18. However, it is noteworthy that orientation changes in area 18 are more variable in amplitude than in area 17. This may be because area 18 receives more diverse inputs than area 17, including a direct input from dLGN (dorsal Lateral Geniculate Nucleus) Y cells. In the third part, we modeled the experimentally observed learning with neural networks using a Hebbian learning rule (networks are self-organizing maps). We have shown that feedback from higher areas to the primary visual cortex was desirable for the neurons orientation selectivity conservation. Overall, this thesis shows the importance of local connections in neuronal plasticity. In particular, they guarantee a homeostatic learning, i.e. maintaining the representativeness of orientations in the cortex. In a complementary manner, it also shows the importance of the superior areas in the conservation of learned orientations.

De nombreuses études montrent que les détecteurs corticaux pourraient résulter de l'application d'un principe de réduction de redondance par indépendance statistique de leurs activités. L'analyse en Composantes Indépendantes est utilisée ici pour générer ces détecteurs, puis leurs performances sont analysées en terme de codage et de description pour catégoriser des images sémantiquement. La propriété d'indépendance statistique permet notamment de vaincre la « malédiction de la dimension » dans un contexte de classification d'images. Un second volet concerne la sémantique des images et la perception visuelle. Des sujets humains sont confrontés à des séries d'expérimentation, captant leur jugement de similarités visuelles, afin de pouvoir identifier les catégories sémantiques, d'apprécier l'apport de modalités perceptives comme la chrominance versus la luminance, et de mettre en évidence des asymétries perceptives.

Made available in DSpace on 2016-08-17T14:52:43Z (GMT). No. of bitstreams: 1 Andre Borges Cavalcante.pdf: 1739525 bytes, checksum: 2073615c7df203b086d5c76276905a35 (MD5) Previous issue date: 2008-02-25 / Efficient coding of natural images yields filters similar to the Gabor-like receptive fields of simple cells of primary visual cortex. However, natural and man-made images have different statistical proprieties. Here we show that a simple theoretical analysis of power spectra in a sparse model suggests that natural and man-made images would need specific filters for each group. Indeed, when applying sparse coding to man-made scenes, we found both Gabor and Haar wavelet-like filters. Furthermore, we found that man-made images when projected on those filters yielded smaller mean squared error than when projected on Gabor-like filters only. Thus, as natural and man-made images require different filters to be efficiently represented, these results suggest that besides Gabor, the primary visual cortex should also have cells with Haar-like receptive fields. / A codificação eficiente de imagens naturais gera filtros similares às wavelets de Gabor que relembram os campos receptivos de células simples do córtex visual primário. No entanto, imagens naturais e urbanas tem características estatísticas diferentes. Será mostrado que uma simples análise do espectro de potência em um modelo eficiente sugere que imagens naturais e urbanas requerem filtros específicos para cada grupo. De fato, aplicando codificação eficiente à imagens urbanas, encontramos filtros similares às wavelets de Gabor e de Haar. Além disso, observou-se que imagens urbanas quando projetadas nesses filtros geraram um menor erro médio quadrático do que quando projetadas somente em filtros de similares a Gabor. Desta forma, como imagens naturais e urbanas requerem filtros diferentes para serem representadas de forma eficiente, estes resultados sugerem que além de Gabor, o córtex visual primário também deve possuir células com campos receptivos similares às wavelets de Haar.

<p><a></a><a>The visual system is capable of interpreting immense sensory complexity, allowing us to quickly identify behaviorally relevant stimuli in the environment. It performs this task with a hierarchical organization that works to detect, relay, and integrate visual stimulus features into an interpretable form. To understand the complexities of this system, visual neuroscientists have benefited from the many advantages of using mice as visual models. Despite their poor visual acuity, these animals possess surprisingly complex visual systems, and have been instrumental in understanding how visual features are processed in the primary visual cortex (V1). However, a growing body of literature has shown that primary sensory areas like V1 are capable of more than basic feature detection, but can express neural activity patterns related to learning, memory, categorization, and prediction. </a></p> <p>Visual experience fundamentally changes the encoding and perception of visual stimuli at many scales, and allows us to become familiar with environmental cues. However, the neural processes that govern visual familiarity are poorly understood. By exposing awake mice to repetitively presented visual stimuli over several days, we observed the emergence of low frequency oscillations in the primary visual cortex (V1). The oscillations emerged in population level responses known as visually evoked potentials (VEPs), as well as single-unit responses, and were not observed before the perceptual experience had occurred. They were also not evoked by novel visual stimuli, suggesting that they represent a new form of visual familiarity in the form of low frequency oscillations. The oscillations also required the muscarinic acetylcholine receptors (mAChRs) for their induction and expression, highlighting the importance of the cholinergic system in this learning and memory-based phenomenon. Ongoing visually evoked oscillations were also shown to increase the VEP amplitude of incoming visual stimuli if the stimuli were presented at the high excitability phase of the oscillations, demonstrating how neural activity with unique temporal dynamics can be used to influence visual processing.</p> <p>Given the necessity of perceptual experience for the strong expression of these oscillations and their dependence on the cholinergic system, it was clear we had discovered a phenomenon grounded in visual learning or memory. To further validate this, we characterized this response in a mouse model of Fragile X syndrome (FX), the most common inherited form of autism and a condition with known visual perceptual learning deficits. Using a multifaceted experimental approach, a number of neurophysiological differences were found in the oscillations displayed in FX mice. Extracellular recordings revealed shorter durations and lower power oscillatory activity in FX mice. Furthermore, we found that the frequency of peak oscillatory activity was significantly decreased in FX mice, demonstrating a unique temporal neural impairment not previously reported in FX. In collaboration with Dr. Christopher J. Quinn at Purdue, we performed functional connectivity analysis on the extracellularly recorded spikes from WT and FX mice. This analysis revealed significant impairments in functional connections from multiple layers in FX mice after the perceptual experience; some of which were validated by another graduate student (Qiuyu Wu) using Channelrhodopsin-2 assisted circuit mapping (CRACM). Together, these results shed new light on how visual stimulus familiarity is differentially encoded in FX via persistent oscillations, and allowed us to identify impairments in cross layer connectivity that may underlie these differences. </p> <p>Finally, we asked whether these oscillations are observable in other brain areas or are intrinsic to V1. Furthermore, we sought to determine if the oscillating unit populations in V1 possess uniform firing dynamics, or contribute differentially to the population level response. By performing paired recordings, we did not find prominent oscillatory activity in two visual thalamic nuclei (dLGN and LP) or a nonvisual area (RSC) connected to V1, suggesting the oscillations may not propagate with similar dynamics via cortico-thalamic connections or retrosplenial connections, <a>but may either be uniquely distributed across the visual hierarchy or predominantly</a> restricted to V1. Using K-means clustering on a large population of oscillating units in V1, we found unique temporal profiles of visually evoked responses, demonstrating distinct contributions of different unit sub-populations to the oscillation response dynamics.</p>

<div>The visual system is responsible for processing visual input, inferring its environmental causes, and assessing its behavioral significance that eventually relates to visual perception and guides animal behavior. There is emerging evidence that visual perception does not simply mirror the outside world but is heavily influenced by contextual information. Specifically, context might refer to the sensory, cognitive, and/or behavioral cues that help to assess the behavioral relevance of image features. One of the most famous examples of such behavior is visual or optical illusions. These illusions contain sensory cues that induce a subjective percept that is not aligned with the physical nature of the stimulation, which, in turn, suggests that a visual system is not a passive filter of the outside world but rather an active inference machine.</div><div>Such robust behavior of the visual system is achieved through intricate neural computations spanning several brain regions that allow dynamic visual processing. Despite the numerous attempts to gain insight into those computations, it has been challenging to decipher the circuit-level implementation of contextual processing due to technological limitations. These questions are of great importance not only for basic research purposes but also for gaining deeper insight into neurodevelopmental disorders that are characterized by altered sensory experiences. Recent advances in genetic engineering and neurotechnology made the mouse an attractive model to study the visual system and enabled other researchers and us to gain unprecedented cellular and circuit-level insights into neural mechanisms underlying contextual processing.</div><div>We first investigated how familiarity modifies the neural representation of stimuli in the mouse primary visual cortex (V1). Using silicon probe recordings and pupillometry, we probed neural activity in naive mice and after animals were exposed to the same stimulus over the course of several days. We have discovered that familiar stimuli evoke low-frequency oscillations in V1. Importantly, those oscillations were specific to the spatial frequency content of the familiar stimulus. To further validate our findings, we investigated how this novel form of visual learning is represented in serotonin-transporter (SERT) deficient mice. These transgenic animals have been previously found to have various neurophysiological alterations. We found that SERT-deficient animals showed longer oscillatory spiking activity and impaired cortical tuning after visual learning. Taken together, we discovered a novel phenomenon of familiarity-evoked oscillations in V1 and utilized it to reveal altered perceptual learning in SERT-deficient mice.</div><div>16</div><div>Next, we investigated how spatial context influences sensory processing. Visual illusions provide a great opportunity to investigate spatial contextual modulation in early visual areas. Leveraging behavioral training, high-density silicon probe recordings, and optogenetics, we provided evidence for an interplay of feedforward and feedback pathways during illusory processing in V1. We first designed an operant behavioral task to investigate illusory perception in mice. Kanizsa illusory contours paradigm was then adapted from primate studies to mouse V1 to elucidate neural correlates of illusory responses in V1. These experiments provided behavioral and neurophysiological evidence for illusory perception in mice. Using optogenetics, we then showed that suppression of the lateromedial area inhibits illusory responses in mouse V1. Taken together, we demonstrated illusory responses in mice and their dependence on the top-down feedback from higher-order visual areas.</div><div>Finally, we investigated how temporal context modulates neural responses by combining silicon probe recordings and a novel visual oddball paradigm that utilizes spatial frequency filtered stimuli. Our work extended prior oddball studies by investigating how adaptation and novelty processing depends on the tuning properties of neurons and their laminar position. Furthermore, given that reduced adaptation and sensory hypersensitivity are one of the hallmarks of altered sensory experiences in autism, we investigated the effects of temporal context on visual processing in V1 of a mouse model of fragile X syndrome (FX), a leading monogenetic cause of autism. We first showed that adaptation was modulated by tuning properties of neurons in both genotypes, however, it was more confined to neurons preferring the adapted feature in FX mice. Oddball responses, on the other hand, were modulated by the laminar position of the neurons in WT with the strongest novelty responses in superficial layers, however, they were uniformly distributed across the cortical column in FX animals. Lastly, we observed differential processing of omission responses in FX vs. WT mice. Overall, our findings suggest that reduced adaptation and increased oddball processing might contribute to altered perceptual experiences in FX and autism.</div>

The ability of the visual system for object recognition is remarkable. A better understanding of its processing would lead to better computer vision systems and could improve our understanding of the underlying principles which produce intelligence. We propose a computational model of the visual areas V1 and V2, implementing a rich connectivity inspired by the neocortical circuit. We combined the three most important cortical plasticity mechanisms. 1) Hebbian synaptic plasticity to learn the synapse strengths of excitatory and inhibitory neurons, including trace learning to learn invariant representations. 2) Intrinsic plasticity to regulate the neurons responses and stabilize the learning in deeper layers. 3) Structural plasticity to modify the connections and to overcome the bias for the learnings from the initial definitions. Among others, we show that our model neurons learn comparable receptive fields to cortical ones. We verify the invariant object recognition performance of the model. We further show that the developed weight strengths and connection probabilities are related to the response correlations of the neurons. We link the connection probabilities of the inhibitory connections to the underlying plasticity mechanisms and explain why inhibitory connections appear unspecific. The proposed model is more detailed than previous approaches. It can reproduce neuroscientific findings and fulfills the purpose of the visual system, invariant object recognition. / Das visuelle System des Menschen hat die herausragende Fähigkeit zur invarianten Objekterkennung. Ein besseres Verständnis seiner Arbeitsweise kann zu besseren Computersystemen für das Bildverstehen führen und könnte darüber hinaus unser Verständnis von den zugrundeliegenden Prinzipien unserer Intelligenz verbessern. Diese Arbeit stellt ein Modell der visuellen Areale V1 und V2 vor, welches eine komplexe, von den Strukturen des Neokortex inspirierte, Verbindungsstruktur integriert. Es kombiniert die drei wichtigsten kortikalen Plastizitäten: 1) Hebbsche synaptische Plastizität, um die Stärke der exzitatorischen und inhibitorischen Synapsen zu lernen, welches auch „trace“-Lernen, zum Lernen invarianter Repräsentationen, umfasst. 2) Intrinsische Plastizität, um das Antwortverhalten der Neuronen zu regulieren und damit das Lernen in tieferen Schichten zu stabilisieren. 3) Strukturelle Plastizität, um die Verbindungen zu modifizieren und damit den Einfluss anfänglicher Festlegungen auf das Lernergebnis zu reduzieren. Neben weiteren Ergebnissen wird gezeigt, dass die Neuronen des Modells vergleichbare rezeptive Felder zu Neuronen des visuellen Kortex erlernen. Ebenso wird die Leistungsfähigkeit des Modells zur invariante Objekterkennung verifiziert. Des Weiteren wird der Zusammenhang von Gewichtsstärke und Verbindungswahrscheinlichkeit zur Korrelation der Aktivitäten der Neuronen aufgezeigt. Die gefundenen Verbindungswahrscheinlichkeiten der inhibitorischen Neuronen werden in Zusammenhang mit der Funktionsweise der inhibitorischen Plastizität gesetzt, womit erklärt wird warum inhibitorische Verbindungen unspezifisch erscheinen. Das vorgestellte Modell ist detaillierter als vorangegangene Arbeiten. Es ermöglicht neurowissenschaftliche Erkenntnisse nachzuvollziehen, wobei es ebenso die Hauptleistung des visuellen Systems erbringt, invariante Objekterkennung. Darüber hinaus ermöglichen sein Detailgrad und seine Selbstorganisationsprinzipien weitere neurowissenschaftliche Erkenntnisse und die Modellierung komplexerer Modelle der Verarbeitung im Gehirn.

There is a difference between the raw sensory input to the brain and our stable perception of entities in the environment. A first approach to investigate perception is to study relationships between properties of currently presented stimuli and biological correlates of perceptual processes. However, it is known that such processes are not only dependent on the current stimulus. Sampling of information and the concurrent neuronal processing of stimulus content rely on contextual relationships in the environment, and between the environment and the body. Perceptual processes dynamically adjust to relevant context, such as the current task of the organism and its immediate history. To understand perception, we have to study how processing of current stimulus content is influenced by such contextual factors. This thesis investigates the influence of such factors on visual processing. In particular, it investigates effects of temporal context in early visual processing and the effect of task context in eye movement control. To investigate effects of contextual factors on early visual processing of current stimulus content, we study neuronal processing of visual information in the primary visual cortex. We use real-time optical imaging with voltage sensitive dyes to capture neuronal population activity in the millisecond range across several millimeters of cortical area. To characterize the cortical layout concerning the mapping of orientation, previous to further investigations, we use smoothly moving grating stimuli. Investigating responses to this stimulus type systematically, we find independent encoding of local contrast and orientation, and a direct mapping of current stimulus content onto cortical activity (Study 1). To investigate the influence of the previous stimulus as context on processing of current stimulus content, we use abrupt visual changes in sequences of modified natural images. In earlier studies, investigating relatively fast timescales, it was found that the primary visual cortex continuously represents current input (ongoing encoding), with little interference from past stimuli. We investigate whether this coding scheme generalizes to cases in which stimuli change more slowly, as frequently encountered in natural visual input. We use sequences of natural scene contours, comprised of vertically and horizontally filtered natural images, their superpositions, and a blank stimulus, presented with 10 or 33 Hz. We show that at the low temporal frequency, cortical activity patterns do not encode the present orientations but instead reflect their relative changes in time. For example, when a stimulus with horizontal orientation is followed by the superposition of both orientations, the pattern of cortical activity represents the newly added vertical orientations instead of the full sum of orientations. Correspondingly, contour removal from the superposition leads to the representation of orientations that have disappeared rather than those that remain. This is in sharp contrast to more rapid sequences for which we find an ongoing representation of present input, consistent with earlier studies. In summary, we find that for slow stimulus sequences, populations of neurons in the primary visual cortex are no longer tuned to orientations within individual stimuli but instead represent the difference between consecutive stimuli. Our results emphasize the influence of the temporal context on early visual processing and consequentially on information transmission to higher cortical areas (Study 2). To study effects of contextual factors on the sampling of visual information, we focus on human eye movement control. The eyes are actively moved to sample visual information from the environment. Some traditional approaches predict eye movements solely on simple stimulus properties, such as local contrasts (stimulus-driven factors). Recent arguments, however, emphasize the influence of tasks (task context) and bodily factors (spatial bias). To investigate how contextual factors affect eye movement control, we quantify the relative influences of the task context, spatial biases and stimulus-driven factors. Participants view and classify natural scenery and faces while their eye movements are recorded. The stimuli are composed of small image patches. For each of these patches we derive a measure that quantifies stimulus-driven factors, based on the image content of a patch, and spatial viewing biases, based on the location of the patch. Utilizing the participants’ classification responses, we additionally derive a measure, which reflects the information content of a patch in the context of a given task. We show that the effect of spatial biases is highest, that task context is a close runner-up, and that stimulus-driven factors have, on average, a smaller influence. Remarkably, all three factors make independent and significant contributions to the selection of viewed locations. Hence, in addition to stimulus-driven factors and spatial biases, the task context contributes to visual sampling behavior and has to be considered in a model of human eye movements. Visual processing of current stimulus content, in particular visual sampling behavior and early processing, is inherently dependent on context. We show that already in the first cortical stage, temporal context strongly affects the processing of new visual information and that visual sampling by eye movements is significantly influenced by the task context, independently of spatial factors and stimulus-driven factors. The empirical results presented provide foundations for an improved theoretical understanding of the role of context in perceptual processes.