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.
Neuro-Electronics Research Flanders (NERF), IMEC, Leuven, Belgium; Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium; VIB, Leuven, Belgium.
Cell Rep. 2018 Dec 4;25(10):2635-2642.e5. doi: 10.1016/j.celrep.2018.11.033.
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.
从特定脑回路中的大规模群体尖峰活动中揭示空间表示，为闭环实验提供了有价值的反馈。我们开发了一个基于图形处理单元（GPU）的群体解码系统，用于从啮齿动物的未排序时空尖峰模式中快速重建空间位置，无论是在运行行为还是在睡眠期间。与优化的四核中央处理单元（CPU）实现相比，我们的方法在八个测试的大鼠海马体、皮层和丘脑群体记录中实现了约 20 到 50 倍的速度提升，具有实时解码速度（每个尖峰约几毫秒分之一）和可扩展性高达数千个通道。通过实时容纳并行洗牌（计算时间<15 毫秒），我们的方法能够评估安静清醒或睡眠期间在线解码的“记忆重放”候选者的统计显著性。这个开源软件工具包支持在闭环神经科学实验中解码空间相关性或内容触发的实验操作。
