@article {3990,
	title = {Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes},
	journal = {Cell Reports},
	volume = {25},
	year = {2018},
	month = {Jan-12-2018},
	pages = {2635 - 2642.e5},
	abstract = {<p>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{\textquoteright} 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 \&lt;15 ms), our approach enables assessment of the statistical significance of online-decoded {\textquotedblleft}memory replay{\textquotedblright} 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.</p>
},
	keywords = {GPU, memory replay, neural decoding, place codes, population decoding, spatiotemporal patterns},
	issn = {22111247},
	doi = {https://doi.org/10.1016/j.celrep.2018.11.033},
	url = {https://www.sciencedirect.com/science/article/pii/S2211124718317960},
	author = {Hu, Sile and Ciliberti, Davide and Grosmark, Andres D. and Michon, {\'e}d{\'e}ric and Ji, Daoyun and Hector Penagos and {\'a}ki, {\"o}rgy and Matthew A. Wilson and Kloosterman, Fabian and Chen, Zhe}
}
