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dc.contributor.advisorChoe, Yoonsuck
dc.creatorYu, Yingwei
dc.date.accessioned2010-01-15T00:13:52Z
dc.date.accessioned2010-01-16T02:08:32Z
dc.date.available2010-01-15T00:13:52Z
dc.date.available2010-01-16T02:08:32Z
dc.date.created2006-08
dc.date.issued2009-06-02
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-1762
dc.description.abstractNeurons are connected to form functional networks in the brain. When neurons are combined in sequence, nontrivial effects arise. One example is disinhibition; that is, inhibition to another inhibitory factor. Disinhibition may be serving an important purpose because a large number of local circuits in the brain contain disinhibitory connections. However, their exact functional role is not well understood. The objective of this dissertation is to analyze the computational role of disinhibition in brain function, especially in visual perception and attentional control. My approach is to propose computational models of disinhibition and then map the model to the local circuits in the brain to explain psychological phenomena. Several computational models are proposed in this dissertation to account for disinhibition. (1) A static inverse difference of Gaussian filter (IDoG) is derived to account explicitly for the spatial effects of disinhibition. IDoG can explain a number of complex brightness-contrast illusions, such as the periphery problem in the Hermann grid and the White's effect. The IDoG model can also be used to explain orientation perception of multiple lines as in the modified version of Poggendorff illusion. (2) A spatio-temporal model (IDoGS) in early vision is derived and it successfully explains the scintillating grid illusion, which is a stationary display giving rise to a striking, dynamic, scintillating effect. (3) An interconnected Cohen-Grossberg neural network model (iCGNN) is proposed to address the dynamics of disinhibitory neural networks with a layered structure. I derive a set of sufficient conditions for such an interconnected system to reach asymptotic stability. (4) A computational model combining recurrent and feed-forward disinhibition is designed to account for input-modulation in temporal selective attention. The main contribution of this research is that it developed a unified framework of disinhibition to model several different kinds of neural circuits to account for various perceptual and attentional phenomena. Investigating the role of disinhibition in the brain can provide us with a deeper understanding of how the brain can give rise to intelligent and complex functions.en
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectdisinhibitionen
dc.subjectvisual perceptionen
dc.subjectcognitive modelingen
dc.subjectscintillating griden
dc.subjectpoggendorff illusionen
dc.subjectStroop effecten
dc.titleComputational role of disinhibition in brain functionen
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentComputer Scienceen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberGutierrez-Osuna, Ricardo
dc.contributor.committeeMemberIoerger, Thomas R.
dc.contributor.committeeMemberYamauchi, Takashi
dc.type.genreElectronic Dissertationen
dc.type.materialtexten
dc.format.digitalOriginborn digitalen


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