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

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

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Animals
  • Computer Graphics
  • Hippocampus / physiology
  • Memory
  • Neurons / physiology*
  • Rats
  • Silicon

Substances

  • Silicon