Title:
A sparse coding model of V1 produces surround suppression effects in response to natural scenes

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dc.contributor.author Del Giorno, Allie en_US
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2013-05-10T13:06:48Z
dc.date.available 2013-05-10T13:06:48Z
dc.date.issued 2013-05-08
dc.description.abstract Recent electrophysiology research has made significant advancements toward revealing the neural basis of early visual processing. The brain is optimized to draw conclusions from natural scenes, and models of the human visual system may uncover principles by which to develop better automated vision systems. In turn, the neuroscience community would benefit from deeper understanding of human vision through the implementation and testing of models of this neural system. While many neural coding models have been proposed for the primary visual cortex (V1), it remains an open question as to which model best describes the diversity of observed response properties. For instance, the canonical linear-nonlinear model (LN) partially explains some fundamental mechanistic and phenomenological properties of V1, but is unable to explain many nonlinear response properties that are likely associated with the keys to efficient and robust human vision. Surround suppression is one such nonlinear response property in which visual stimuli extending beyond the classical receptive field (CRF) selectively diminish neural responses. This property has been studied through electrophysiology experiments with synthetic stimuli (e.g., gratings). Surprisingly, high level sparse coding models implemented in a biologically plausible dynamical system have been shown to produce surround suppression effects that match individual and population observed responses. More recently, surround suppression has been investigated experimentally using natural stimuli, and these experiments have shown an increase in the sparsity of measured responses. Despite these findings, it remains unclear whether a functional sparse coding model is sufficient to produce the types of surround suppression observed with natural stimuli. This thesis demonstrates that the surround suppression effects recently observed with natural stimuli are also emergent properties of a sparse coding model. First, relevant literature in human vision and signal processing will be reviewed. The methods for implementing the model and the results from simulations will then be presented followed by discussion of implications of these results and future work. en_US
dc.description.advisor Butera, Robert - Faculty Mentor; Rozell, Christopher - Faculty Mentor en_US
dc.identifier.uri http://hdl.handle.net/1853/46891
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Vision en_US
dc.subject Neuroscience en_US
dc.subject Signal processing en_US
dc.subject Efficient coding en_US
dc.title A sparse coding model of V1 produces surround suppression effects in response to natural scenes en_US
dc.type Text
dc.type.genre Undergraduate Thesis
dspace.entity.type Publication
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
local.contributor.corporatename Undergraduate Research Opportunities Program
local.relation.ispartofseries Undergraduate Research Option Theses
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication 0db885f5-939b-4de1-807b-f2ec73714200
relation.isSeriesOfPublication e1a827bd-cf25-4b83-ba24-70848b7036ac
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