Machine learning for neural decoding

Joshua I. Glaser*, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich, Lee E. Miller, Konrad P. Kording

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outper-form traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help to advance engineering applications such as brain–machine interfaces. Our code package is available at github.com/kordinglab/neural_decoding.

Original languageEnglish (US)
Article numberENEURO.0506-19.2020
Pages (from-to)1-16
Number of pages16
JournaleNeuro
Volume7
Issue number4
DOIs
StatePublished - Jul 1 2020

Keywords

  • Deep learning
  • Hippocampus
  • Machine learning
  • Motor cortex
  • Neural data analysis
  • Neural decoding
  • Somatosensory cortex

ASJC Scopus subject areas

  • Neuroscience(all)

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