Title:
Learning distributed representations in the human brain

dc.contributor.author Schapiro, Anna
dc.contributor.corporatename Georgia Institute of Technology. School of Psychology en_US
dc.contributor.corporatename University of Pennsylvania. Dept. of Psychology en_US
dc.date.accessioned 2020-12-01T21:04:30Z
dc.date.available 2020-12-01T21:04:30Z
dc.date.issued 2020-11-19
dc.description Presented online on November 19, 2020 at 11:00 a.m. en_US
dc.description Anna Schapiro is an Assistant Professor in the Department of Psychology at the University of Pennsylvania. Her research combines neural network modeling and empirical methods (fMRI, EEG, patient studies, behavior) to uncover learning algorithms and principles of how memories of regularities in the environment come to be represented throughout the brain. en_US
dc.description Runtime: 62:18 minutes en_US
dc.description.abstract The remarkable success of neural network models in machine learning has relied on the use of distributed representations — activity patterns that overlap across related inputs. Under what conditions does the brain also rely on distributed representations for learning? There are benefits and costs to this form of representation: it allows rapid, efficient learning and generalization, but is highly susceptible to interference. We recently developed a neural network model of the hippocampus that proposes that one subregion (CA1) may employ this form of representation, complementing known pattern-separated representations in other subregions. This provides an exciting domain to test ideas about learning with distributed representations, as the hippocampus learns much more quickly than the neocortical areas that have often been proposed to contain these representations. I will present modeling and empirical work that provide support for the idea that parts of the hippocampus do indeed learn using distributed representations. I will also present ideas about how hippocampal and neocortical areas may interact during sleep to further transform these representations over time. en_US
dc.format.extent 62:18 minutes
dc.identifier.uri http://hdl.handle.net/1853/63945
dc.language.iso en_US en_US
dc.relation.ispartofseries Psychology Colloquium
dc.subject Hippocampus en_US
dc.subject Learning en_US
dc.subject Neural network modeling en_US
dc.title Learning distributed representations in the human brain en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Psychology
local.relation.ispartofseries School of Psychology Colloquiua
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication 768a3cd1-8d73-4d47-b418-0fc859ce897d
relation.isSeriesOfPublication da9098fa-29c9-4bda-a0d0-bb2f2a5f2bd0
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