Real-time readout of large-scale unsorted neural ensemble place codes

Sile Hu, Davide Ciliberti, Andres D Grosmark, Frédéric Michon, Daoyun Ji, Hector Penagos, György Buzsáki, Matthew A Wilson, Fabian Kloosterman, Zhe Chen
Cell Reports, 25(10): 2635-2642.e5, 2018

Highlights

• Spike-sorting-free decoding reconstructs the rat’s position with ultrafast speed

• GPU-powered population decoding significantly speeds up multi-core CPU-based system

• GPU computing empowers real-time assessment of decoded “memory replay” candidates

• Open-source software toolkit supports closed-loop content-triggered intervention

Uncovering spatial representations from large-scale ensemble spike activity in specific braincircuits 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.

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