Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes

TitleReal-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes
Publication TypeJournal Article
Year of Publication2018
AuthorsHu, S, Ciliberti, D, Grosmark, AD, Michon, édéric, Ji, D, Penagos, H, áki, örgy, Wilson, MA, Kloosterman, F, Chen, Z
JournalCell Reports
Pagination2635 - 2642.e5
Date PublishedJan-12-2018
KeywordsGPU, memory replay, neural decoding, place codes, population decoding, spatiotemporal patterns

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.

Short TitleCell Reports

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