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dc.creatorRuiz, Vito Manuel
dc.date.accessioned2011-07-08T20:07:14Z
dc.date.available2011-07-08T20:07:14Z
dc.date.created2011-05
dc.date.issued2011-07-08
dc.date.submittedMay 2011
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2011-05-3156
dc.descriptiontext
dc.description.abstractThough adaptational effects are found throughout the visual system, the underlying mechanisms and benefits of this phenomenon are not yet known. In this work, the visual system is modeled as a Deep Belief Network, with a novel “post-training” paradigm (i.e. training the network further on certain stimuli) used to simulate adaptation in vivo. An optional sparse variant of the DBN is used to help bring about meaningful and biologically relevant receptive fields, and to examine the effects of sparsification on adaptation in their own right. While results are inconclusive, there is some evidence of an attractive bias effect in the adapting network, whereby the network’s representations are drawn closer to the adapting stimulus. As a similar attractive bias is documented in human perception as a result of adaptation, there is thus evidence that the statistical properties underlying the adapting DBN also have a role in the adapting visual system, including efficient coding and optimal information transfer given limited resources. These results are irrespective of sparsification. As adaptation has never been tested directly in a neural network, to the author’s knowledge, this work sets a precedent for future experiments.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectDeep belief network
dc.subjectNeural network
dc.subjectGenerative model
dc.subjectAdaptation
dc.subjectVision
dc.subjectEfficient coding
dc.subjectSight
dc.titleAdaptation in a deep network
dc.date.updated2011-07-08T20:07:26Z
dc.identifier.slug2152/ETD-UT-2011-05-3156
dc.description.departmentNeuroscience
dc.type.genrethesis*
thesis.degree.departmentNeuroscience
thesis.degree.disciplineNeuroscience
thesis.degree.grantorUniversity of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Neuroscience


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