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