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Cell Rep. 2018 Dec 4;25(10):2635-2642.e5. doi: 10.1016/j.celrep.2018.11.033.

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

Author information

1
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.
2
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium.
3
Department of Neuroscience, Columbia University Medical Center, New York, NY 10019, USA.
4
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium.
5
Department of Molecular and Cellular Biology, Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.
6
The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02134, USA.
7
The Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA.
8
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium. Electronic address: fabian.kloosterman@nerf.be.
9
Department of Psychiatry, Department of Neuroscience and Physiology, School of Medicine, New York University, New York, NY 10016, USA. Electronic address: zhe.chen@nyulangone.org.

Abstract

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.

KEYWORDS:

GPU; memory replay; neural decoding; place codes; population decoding; spatiotemporal patterns

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