Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes
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Author(s)
Hu, Sile
Ciliberti, Davide
Grosmark, Andres D.
Michon, Frédéric
Ji, Daoyun
Penagos, Hector L.
Buzsáki, György
Wilson, Matthew A.
Kloosterman, Fabian
Chen, Zhe
Date Issued
December 2018
Journal
Cell Reports
Publisher
Elsevier
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)
Version
Final published version
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
MIT Department
Picower Institute for Learning and Memory
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.celrep.2018.11.033
