dc.contributor.author | Hu, Sile | |
dc.contributor.author | Ciliberti, Davide | |
dc.contributor.author | Grosmark, Andres D. | |
dc.contributor.author | Michon, Frédéric | |
dc.contributor.author | Ji, Daoyun | |
dc.contributor.author | Penagos, Hector L. | |
dc.contributor.author | Buzsáki, György | |
dc.contributor.author | Wilson, Matthew A. | |
dc.contributor.author | Kloosterman, Fabian | |
dc.contributor.author | Chen, Zhe | |
dc.date.accessioned | 2019-06-07T16:11:22Z | |
dc.date.available | 2019-06-07T16:11:22Z | |
dc.date.issued | 2018-12 | |
dc.date.submitted | 2018-10 | |
dc.identifier.issn | 2211-1247 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/121220 | |
dc.description.abstract | Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents’ unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time <15 ms), our approach enables assessment of the statistical significance of online-decoded “memory replay” candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments. The hippocampal and neocortical neuronal ensembles encode rich spatial information in navigation. Hu et al. develop computational techniques that accommodate real-time decoding and assessment of large-scale unsorted neural ensemble place codes during running behavior and sleep. Keywords: neural decoding; population decoding; place codes; GPU; memory replay; spatiotemporal patterns | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Grant IIS-130764) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.) (Grant R01-MH118928) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.) (Grant R01-MH092638) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.) (Grant TR01-GM104948) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.) (Grant R21-EY028381) | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Grant CCF-1231216) | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1016/j.celrep.2018.11.033 | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.source | Elsevier | en_US |
dc.title | Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Hu, Sile et al. “Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes.” Cell Reports 25, 10 (December 2018): 2635–2642 © 2018 The Author(s) | en_US |
dc.contributor.department | Picower Institute for Learning and Memory | en_US |
dc.relation.journal | Cell Reports | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2019-02-28T13:41:10Z | |
dspace.orderedauthors | Hu, Sile; Ciliberti, Davide; Grosmark, Andres D.; Michon, Frédéric; Ji, Daoyun; Penagos, Hector; Buzsáki, György; Wilson, Matthew A.; Kloosterman, Fabian; Chen, Zhe | en_US |
dspace.embargo.terms | N | en_US |
dspace.date.submission | 2019-04-04T10:17:41Z | |
mit.journal.volume | 25 | en_US |
mit.journal.issue | 10 | en_US |
mit.license | PUBLISHER_CC | en_US |