The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map

Simple item page
dc.citation.firstpagee1003651en_US
dc.citation.issueNumber6en_US
dc.citation.journalTitlePLoS Computational Biologyen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorArai, Mamikoen_US
dc.contributor.authorBrandt, Vickyen_US
dc.contributor.authorDabaghian, Yurien_US
dc.date.accessioned2014-10-08T21:25:38Zen_US
dc.date.available2014-10-08T21:25:38Zen_US
dc.date.issued2014en_US
dc.description.abstractLearning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (?learn? the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ~150-200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process.en_US
dc.identifier.citationArai, Mamiko, Brandt, Vicky and Dabaghian, Yuri. "The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map." <i>PLoS Computational Biology,</i> 10, no. 6 (2014) Public Library of Science: e1003651. http://dx.doi.org/10.1371/journal.pcbi.1003651.en_US
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pcbi.1003651en_US
dc.identifier.urihttps://hdl.handle.net/1911/77460en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleThe Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Mapen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
journal.pcbi.1003651.pdf
Size:
631.56 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Faculty Publications
CMOR Publications