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Computational Modeling of Human Visual Function using Psychophysics, Deep Neural Networks, and Information Theory

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Computational Modeling of Human Visual Function using Psychophysics, Deep Neural Networks, and Information Theory

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dc.contributor.advisor Malo López, Jesús
dc.contributor.author Li, Qiang
dc.contributor.other Facultat de Física es_ES
dc.date.accessioned 2023-02-28T09:17:26Z
dc.date.available 2023-05-30T04:45:07Z
dc.date.issued 2023 es_ES
dc.date.submitted 17-02-2023 es_ES
dc.identifier.uri https://hdl.handle.net/10550/85545
dc.description.abstract Visual perception is a key to unlocking the secrets of brain functions because most of the information is processed through the early visual system and then transmitted to the high-level cognitive perception brain regions. The brain functions as a self-organizing, bio-dynamic, and chaotic system that receives outside information and then decomposes it into pieces of information that can be processed efficiently and independently. The work connects natural image statistics, psychophysics, deep neural networks, and information theory to perceptual vision systems to explore how vision processes information from the outside world and how the information coupled drives functional connectivity between visual regions and even higher-level brain regions. \\ I am a pre-PhD in the lab of image and signal processing Group and computational visual neuroscience at the University of Valencia. I am interested in computational neuroscience, computational neuroimaging, deep learning, information theory and image/video processing. es_ES
dc.format.extent 179 p. es_ES
dc.language.iso en es_ES
dc.subject perception es_ES
dc.subject deep neural networks es_ES
dc.subject information theory es_ES
dc.subject human vision system es_ES
dc.title Computational Modeling of Human Visual Function using Psychophysics, Deep Neural Networks, and Information Theory es_ES
dc.type doctoral thesis es_ES
dc.subject.unesco UNESCO::FÍSICA es_ES
dc.embargo.terms 3 months es_ES

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