Retinotopic Preservation in Deep Belief Network Visual Learning
| dc.comment.hidden | Image of neuron and visual pathway are public domain | en |
| dc.contributor.author | Lam, Michael | |
| dc.date.accessioned | 2011-04-29T18:16:24Z | |
| dc.date.available | 2011-04-29T18:16:24Z | |
| dc.date.issued | 2011-04-29T18:16:24Z | |
| dc.date.submitted | 2011 | |
| dc.description.abstract | One of the foremost characteristics of the mammalian visual system is the retinotopic mapping observed in the low-level visual processing centres; the spatial pattern of activation in the lateral geniculate nucleus and primary visual cortex corresponds topologically to the pattern of light falling on the retina. Various vision systems have been developed that take advantage of structured input such as retinotopy, however these systems are often not biologically plausible. Using a parsimonious approach for implementing retinotopy, one that is based on the biology of our visual pathway, we run simulations of visual learning using a deep belief network (DBN). Experiments show that we can successfully produce receptive fields and activation maps typical of the LGN and visual cortex respectively. These results may indicate a possible avenue of exploration into discovering the workings of the early visual system (and possibly more) on a neuronal level. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/5894 | |
| dc.language.iso | en | en |
| dc.pending | false | en |
| dc.publisher | University of Waterloo | en |
| dc.subject | Visual Learning | en |
| dc.subject | DBN | en |
| dc.subject | Retinotopy | en |
| dc.subject.program | Computer Science | en |
| dc.title | Retinotopic Preservation in Deep Belief Network Visual Learning | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Mathematics | en |
| uws-etd.degree.department | School of Computer Science | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |