Neuroscience and machine learning often operate at two ends of a spectrum. The former sometimes finds itself entrenched in the details of experimentation, and the latter sometimes finds itself drifting into the expanse of theory. Both fields can mutually coexist, and when they do, have produced invaluable results in computational neuroscience towards more plausible models of biological solutions. This dissertation presents two detailed investigations into the benefits of this interdisciplinary field: a model for cognition and a model for vision. Experiments during these investigations led us to a third result: a new learning approach called neural network tomography. We introduce our universal theory of cognition, Confabulation Theory, and discuss its biological plausibility. Confabulation Theory posits that the cerebral cortex, in conjunction with the thalamus, is implementing a repeated functional architecture of thalamocortical modules, each encoding one attribute which an object in the individual's mental universe may possess. These modules are interconnected with concurrence statistics called knowledge links, are capable of confabulating a state, and are carefully controlled with action commands. We use Confabulation Theory to build a model for natural language processing and present striking results in sentence generation with context. Subsequently, we focus on the task of texture classification, which we argue is a more primitive operation than object recognition, and therefore, appropriate for investigation with the goal of elucidating biology's solution for processing visual stimuli. We develop a hierarchical model for texture classification, carefully informed by neuroscience results, and demonstrate state-of-the-art performance on a challenging texture classification dataset in the context of our human psychophysical experiment. Finally, we survey existing methods in neural network learning and propose a new approach with several valuable theoretical advantages. By rephrasing the task of function approximation as replicating the topology and weights of an existing universal approximator network, we show that several of the drawbacks of classical backpropagation learning can be avoided. We define a new objective function, mean squared curvature (MSC), and demonstrate that minimizing the MSC of the difference between the networks during the replication process produces favorable results and allows networks to be reverse-engineered iteratively

There is evidence that the prevalence of migraine in children and adolescents may be increasing. Current theories of migraine pathophysiology in adults suggest activation of central cortical and brainstem pathways in conjunction with the peripheral trigeminovascular system, which ultimately results in release of neuropeptides, facilitation of central pain pathways, neurogenic inflammation surrounding peripheral vessels, and vasodilatation. Although several risk factors for frequent episodic, chronic, and refractory migraine have been identified, the causes of migraine progression are not known. Migraine pathophysiology has not been fully evaluated in children. In this review, we will first discuss the evidence that early therapeutic interventions in the child or adolescent new onset migraineur, may halt or limit progression and disability. We will then review the evidence suggesting that many adults with chronic or refractory migraine developed their migraine as children or adolescents and may not have been treated adequately with migraine-specific therapy. Finally, we will show that early, appropriate and optimal treatment of migraine during childhood and adolescence may result in disease modification and prevent progression of this disease.