Continuing progress of spike sorting in the era of big data.

Published

Journal Article (Review)

Engineering efforts are currently attempting to build devices capable of collecting neural activity from one million neurons in the brain. Part of this effort focuses on developing dense multiple-electrode arrays, which require post-processing via 'spike sorting' to extract neural spike trains from the raw signal. Gathering information at this scale will facilitate fascinating science, but these dreams are only realizable if the spike sorting procedure and data pipeline are computationally scalable, at or superior to hand processing, and scientifically reproducible. These challenges are all being amplified as the data scale continues to increase. In this review, recent efforts to attack these challenges are discussed, which have primarily focused on increasing accuracy and reliability while being computationally scalable. These goals are addressed by adding additional stages to the data processing pipeline and using divide-and-conquer algorithmic approaches. These recent developments should prove useful to most research groups regardless of data scale, not just for cutting-edge devices.

Full Text

Duke Authors

Cited Authors

  • Carlson, D; Carin, L

Published Date

  • March 8, 2019

Published In

Volume / Issue

  • 55 /

Start / End Page

  • 90 - 96

PubMed ID

  • 30856552

Pubmed Central ID

  • 30856552

Electronic International Standard Serial Number (EISSN)

  • 1873-6882

International Standard Serial Number (ISSN)

  • 0959-4388

Digital Object Identifier (DOI)

  • 10.1016/j.conb.2019.02.007

Language

  • eng