In this thesis, we propose a novel framework, the Generative Adaptive Subspace Self Organizing Map (GASSOM), which utilizes sparsity and temporal slowness in learning invariant feature detectors. Sparsity and temporal slowness have been identified as two critical components in shaping visual receptive fields of neurons in the primary visual cortex of animals with a developed vision processing system, such as primates. Sparsity is inspired by Barlow's efficient coding hypothesis, which posits that neural population responses represent sensory data using as few active neurons as possible. The principle of temporal slowness assumes that neurons adapt to encode information about the environment, which is relatively stable in comparison to the raw sensory signals. Using the GASSOM f...[ Read more ]

In this thesis, we propose a novel framework, the Generative Adaptive Subspace Self Organizing Map (GASSOM), which utilizes sparsity and temporal slowness in learning invariant feature detectors. Sparsity and temporal slowness have been identified as two critical components in shaping visual receptive fields of neurons in the primary visual cortex of animals with a developed vision processing system, such as primates. Sparsity is inspired by Barlow's efficient coding hypothesis, which posits that neural population responses represent sensory data using as few active neurons as possible. The principle of temporal slowness assumes that neurons adapt to encode information about the environment, which is relatively stable in comparison to the raw sensory signals. Using the GASSOM framework we show that temporal slowness can emerge in the model as it tries to learn a better representation of sensory signals, and that incorporating slowness results in representations that exhibit better invariance.

We validate the applicability of the GASSOM framework in tasks that require the learning of invariant visual representations. We incorporate the GASSOM in a framework that jointly learns a neural representation and a behavior, and use it to analyze the functional utility of sparsity. We also use this joint learning framework to explain neurophysiological findings about binocular neurons and coordinated eye movements in rodents. We propose the applicability of the GASSOM as a generic learning algorithm that could be used to form hierarchical organizations of feature extractors that model the information flow in the visual cortex. Specifically, we study the development of motion in depth sensitive units. Finally we extend the GASSOM to the event domain, by constructing a framework for learning invariant feature detectors from stimuli generated using event-driven neuromorphic vision sensors.