date: 2015-12-18T22:31:33Z pdf:docinfo:custom:lastpage: 153 pdf:PDFVersion: 1.3 pdf:docinfo:title: Unlocking neural population non-stationarities using hierarchical dynamics models access_permission:can_print_degraded: true EventType: Poster pdf:docinfo:custom:firstpage: 145 subject: Neural Information Processing Systems http://nips.cc/ dc:format: application/pdf; version=1.3 access_permission:fill_in_form: true pdf:encrypted: false dc:title: Unlocking neural population non-stationarities using hierarchical dynamics models Book: Advances in Neural Information Processing Systems 28 pdf:docinfo:custom:Date: 2015 modified: 2015-12-18T22:31:33Z Description-Abstract: Neural population activity often exhibits rich variability. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as non-stationarity. To better understand the nature of co-variability in neural circuits and their impact on cortical information processing, we introduce a hierarchical dynamics model that is able to capture inter-trial modulations in firing rates, as well as neural population dynamics. We derive an algorithm for Bayesian Laplace propagation for fast posterior inference, and demonstrate that our model provides a better account of the structure of neural firing than existing stationary dynamics models, when applied to neural population recordings from primary visual cortex. cp:subject: Neural Information Processing Systems http://nips.cc/ pdf:docinfo:subject: Neural Information Processing Systems http://nips.cc/ pdf:docinfo:custom:Created: 2015 pdf:docinfo:creator: Mijung Park, Gergo Bohner, Jakob H. Macke meta:author: Mijung Park, Gergo Bohner, Jakob H. Macke access_permission:extract_for_accessibility: true lastpage: 153 pdf:docinfo:custom:Type: Conference Proceedings Editors: C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett and R. Garnett Author: Mijung Park, Gergo Bohner, Jakob H. Macke producer: PyPDF2 pdf:docinfo:producer: PyPDF2 pdf:docinfo:custom:Description: Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/) pdf:unmappedUnicodeCharsPerPage: 0 Description: Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/) access_permission:modify_annotations: true firstpage: 145 dc:creator: Mijung Park, Gergo Bohner, Jakob H. Macke pdf:docinfo:custom:EventType: Poster Last-Modified: 2015-12-18T22:31:33Z dcterms:modified: 2015-12-18T22:31:33Z title: Unlocking neural population non-stationarities using hierarchical dynamics models Last-Save-Date: 2015-12-18T22:31:33Z Created: 2015 pdf:docinfo:modified: 2015-12-18T22:31:33Z Language: en-US pdf:docinfo:custom:Language: en-US pdf:docinfo:custom:Book: Advances in Neural Information Processing Systems 28 meta:save-date: 2015-12-18T22:31:33Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Mijung Park, Gergo Bohner, Jakob H. Macke access_permission:assemble_document: true xmpTPg:NPages: 9 Publisher: Curran Associates, Inc. pdf:charsPerPage: 3092 access_permission:extract_content: true Date: 2015 access_permission:can_print: true Type: Conference Proceedings pdf:docinfo:custom:Editors: C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett and R. Garnett pdf:docinfo:custom:Description-Abstract: Neural population activity often exhibits rich variability. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as non-stationarity. To better understand the nature of co-variability in neural circuits and their impact on cortical information processing, we introduce a hierarchical dynamics model that is able to capture inter-trial modulations in firing rates, as well as neural population dynamics. We derive an algorithm for Bayesian Laplace propagation for fast posterior inference, and demonstrate that our model provides a better account of the structure of neural firing than existing stationary dynamics models, when applied to neural population recordings from primary visual cortex. pdf:docinfo:custom:Published: 2015 Published: 2015 pdf:docinfo:custom:Publisher: Curran Associates, Inc. access_permission:can_modify: true