PT  - JOURNAL ARTICLE
AU  - Joshua I. Glaser
AU  - Ari S. Benjamin
AU  - Raeed H. Chowdhury
AU  - Matthew G. Perich
AU  - Lee E. Miller
AU  - Konrad P. Kording
TI  - Machine Learning for Neural Decoding
AID  - 10.1523/ENEURO.0506-19.2020
DP  - 2020 Jul 01
TA  - eneuro
PG  - ENEURO.0506-19.2020
VI  - 7
IP  - 4
4099  - http://www.eneuro.org/content/7/4/ENEURO.0506-19.2020.short
4100  - http://www.eneuro.org/content/7/4/ENEURO.0506-19.2020.full
SO  - eNeuro2020 Jul 01; 7
AB  - 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 outperform 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.