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Environ, 2% des enfants de moins de 7 ans sont porteurs d'un strabisme (Williams et al., 2008) entraînant une perturbation de leur système visuel. Dans un premier temps, nous allons étudier les mouvements oculaires pendant la lecture d'un texte et pendant la poursuite d'une cible chez des enfants strabiques et de comparer ces résultats à des enfants non strabiques du même âge. Dans un second temps, nous avons étudié l'équilibre postural en condition de simple et de double tâche, puis nous avons examiné le rôle des informations proprioceptives sur le contrôle postural chez ces enfants. Nous émettons l'hypothèse selon laquelle le déficit visuel des enfants strabiques est à l'origine d'un retard ou d'un mauvais traitement cognitif de l'information visuelle ainsi qu'à l'origine d'un développement moteur modifiés s'appuyant sur d'autres systèmes sensoriels pour compenser leur déficit visuel. Quatre études publiées dans des revues internationales ont été réalisés pour tester ces hypothèses. L'ensemble de ces travaux a été réalisé afin d'apporter une meilleure compréhension des mécanismes et des interactions entre oculomotricité et contrôle postural chez les enfants strabiques. Ceci permet d'apporter des éléments fondés à l'aide au diagnostic, à la prise en charge rééducative mais aussi chirurgical de ces enfants strabiques. / Approximately 2% of children under 7 years old suffer strabismus (Williams et al., 2008), leading to a deficit in their visuel system. Firstly, we studied eye movements during reading and during smooth pursuit in strabismic children and compared these results to non strabismic age-matched children. Secondly, we studied postural control in both simple and double task, and the role of proprioceptive information on postural control in these children. We hypothesize that visual deficit in strabismic children delayed cognitive processing of visual information, and modified motor development by using other sensory systems to compensate their visual deficit. Four peer reviews were conducted to confirm these assumptions. Taken together, these studies provide a better understanding about mechanisms and interactions between oculomotricity and postural control in strabismic children. These findings allow to bring evidence for improve the diagnosis, rehabilitation treatment and also surgical treatment of strabismic children.

Advances in unsupervised learning and deep neural networks have led to increased performance in a number of domains, and to the ability to draw strong comparisons between the biological method of self-organization conducted by the brain and computational mechanisms. This thesis aims to use real-world data to tackle two areas in the domain of computer vision which have biological equivalents: feature detection and motion tracking. The aforementioned advances have allowed efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcrafted features. The first part of this thesis evaluates such representations by comparing regularization and preprocessing methods which incorporate local neighbouring information during training on a single-layer neural network. The networks are trained and tested on the Hollywood2 video dataset, as well as the static CIFAR-10, STL-10, COIL-100, and MNIST image datasets. The induction of topography or simple image blurring via Gaussian filters during training produces better discriminative features as evidenced by the consistent and notable increase in classification results that they produce. In the visual domain, invariant features are desirable such that objects can be classified despite transformations. It is found that most of the compared methods produce more invariant features, however, classification accuracy does not correlate to invariance. The second, and paramount, contribution of this thesis is a biologically-inspired model to explain the emergence of motion tracking behaviour in early development using unsupervised learning. The model’s self-organization is biased by an original concept called retinal constancy, which measures how similar visual contents are between successive frames. In the proposed two-layer deep network, when exposed to real-world video, the first layer learns to encode visual motion, and the second layer learns to relate that motion to gaze movements, which it perceives and creates through bi-directional nodes. This is unique because it uses general machine learning algorithms, and their inherent generative properties, to learn from real-world data. It also implements a biological theory and learns in a fully unsupervised manner. An analysis of its parameters and limitations is conducted, and its tracking performance is evaluated. Results show that this model is able to successfully follow targets in real-world video, despite being trained without supervision on real-world video.