Title:
Learning distributed representations in the human brain
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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