Machine learning for neural decoding
This work helps neuroscientists and engineers improve neural decoding for better understanding of neural information and advancing brain-machine interfaces, but it is incremental as it applies existing methods to a known problem.
The paper addresses the underutilization of modern machine learning in neural decoding by providing a tutorial and performance comparisons, showing that neural networks and ensembles significantly outperform traditional methods like Wiener and Kalman filters in decoding spiking activity across brain regions.
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 advance engineering applications such as brain machine interfaces.