Show simple item record

dc.contributor.advisorPillow, Jonathan W.en
dc.contributor.advisorMiikkulainen, Ristoen
dc.creatorRuiz, Vito Manuelen
dc.date.accessioned2011-07-08T20:07:14Zen
dc.date.available2011-07-08T20:07:14Zen
dc.date.issued2011-05en
dc.date.submittedMay 2011en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2011-05-3156en
dc.descriptiontexten
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.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectDeep belief networken
dc.subjectNeural networken
dc.subjectGenerative modelen
dc.subjectAdaptationen
dc.subjectVisionen
dc.subjectEfficient codingen
dc.subjectSighten
dc.titleAdaptation in a deep networken
dc.date.updated2011-07-08T20:07:26Zen
dc.identifier.slug2152/ETD-UT-2011-05-3156en
dc.contributor.committeeMemberFiete, Ilaen
dc.contributor.committeeMemberGeisler, Wilsonen
dc.contributor.committeeMemberSeidemann, Eyalen
dc.description.departmentNeuroscienceen
dc.type.genrethesisen
thesis.degree.departmentNeuroscienceen
thesis.degree.disciplineNeuroscienceen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Neuroscienceen


Files in this item

Icon

This item appears in the following Collection(s)

Show simple item record