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
Affiliations
Sile Hu
Department of Instrument Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310027, China; Department of Psychiatry, Department of Neuroscience and Physiology, School of Medicine, New York University, New York, NY 10016, USA
Davide Ciliberti
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium
Andres D. Grosmark
Department of Neuroscience, Columbia University Medical Center, New York, NY 10019, USA
Frédéric Michon
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium
Daoyun Ji
Department of Molecular and Cellular Biology, Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
Hector Penagos
The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02134, USA
György Buzsáki
The Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA
Matthew A. Wilson
The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02134, USA
Fabian Kloosterman
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium; Corresponding author
Zhe Chen
Department of Psychiatry, Department of Neuroscience and Physiology, School of Medicine, New York University, New York, NY 10016, USA; Corresponding author
Summary: 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. : The hippocampal and neocortical neuronal ensembles encode rich spatial information in navigation. Hu et al. develop computational techniques that accommodate real-time decoding and assessment of large-scale unsorted neural ensemble place codes during running behavior and sleep. Keywords: neural decoding, population decoding, place codes, GPU, memory replay, spatiotemporal patterns
