Biologically Plausible Neural Learning using Symmetric Predictive Estimators
dc.contributor.author | Xu, David | |
dc.date.accessioned | 2016-08-04T16:01:04Z | |
dc.date.available | 2016-08-04T16:01:04Z | |
dc.date.issued | 2016-08-04 | |
dc.date.submitted | 2016-07-21 | |
dc.description.abstract | A predictive estimator (PE) is a neural microcircuit hypothesized to explain how the brain processes certain types of information. They participate in a hierarchy, passing predictions to lower layers, which send back prediction errors. Meanwhile, the network learns optimal connection weights in order to minimize the prediction errors. This two-way process has been used to hypothesize models for brain mechanisms, such as visual information processing. However, the standard implementation for a PE uses the same weight matrix for both feed-forward and feed-back projections, which is not biologically plausible. In this thesis, we investigate the predictive estimator using individual feed-forward and feed-back connection weights. We extend the model and introduce the Symmetric Predictive Estimator (SPE). We investigate the dynamics of a SPE network, analyze its stability, and define a general learning rule. A functional model of the SPE is implemented analytically, and in a neural framework using spiking neurons. Both implementations are built as Python modules that can accept generic, numerical inputs. With a series of general experiments, we demonstrate its ability to learn non-linear functions and perform a supervised-learning task. This variation on the PE may provide insights to the theory of predictive estimators and their role in the brain. | en |
dc.identifier.uri | http://hdl.handle.net/10012/10612 | |
dc.language.iso | en | en |
dc.pending | false | |
dc.publisher | University of Waterloo | en |
dc.subject | predictive coding | en |
dc.subject | neural networks | en |
dc.subject | computational neuroscience | en |
dc.subject | predictive estimators | en |
dc.subject | biological plausibility | en |
dc.subject | perceptual network | en |
dc.title | Biologically Plausible Neural Learning using Symmetric Predictive Estimators | en |
dc.type | Master Thesis | en |
uws-etd.degree | Master of Mathematics | en |
uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
uws-etd.degree.discipline | Computer Science | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws.contributor.advisor | Orchard, Jeff | |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.peerReviewStatus | Unreviewed | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.scholarLevel | Graduate | en |
uws.typeOfResource | Text | en |