%0 Journal Article %J Cell Reports %D 2018 %T Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes %A Hu, Sile %A Ciliberti, Davide %A Grosmark, Andres D. %A Michon, édéric %A Ji, Daoyun %A Hector Penagos %A áki, örgy %A Matthew A. Wilson %A Kloosterman, Fabian %A Chen, Zhe %K GPU %K memory replay %K neural decoding %K place codes %K population decoding %K spatiotemporal patterns %X

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.

%B Cell Reports %V 25 %P 2635 - 2642.e5 %8 Jan-12-2018 %G eng %U https://www.sciencedirect.com/science/article/pii/S2211124718317960 %N 10 %! Cell Reports %R https://doi.org/10.1016/j.celrep.2018.11.033