Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting

Abstract

Much attention has been given to directly interpreting neural firing in the primary motor cortex as a force signal, i.e., a signal that correlates with force production in muscles. How to robustly predict EMG patterns from M1 firing and which M1 neurons contribute to a particular muscle behaviour are interesting questions that arise under this hypothesis. From a statistical point of view, this question corresponds to analyzing datasets with a large number of input dimensions to detect which inputs contribute the most to the outputs. This is, at worst, a computationally exhausting combinatorial task. We present a Bayesian Backfitting algorithm that automatically determines the relevant input dimensions in a regression problem. We compare this algorithm to a brute-force approach that considers combinations of relevant input dimensions. The dataset (Sergio & Kalasha, 1998) consists of neuronal firing of M1 neurons and the corresponding muscle EMG data. Bayesian Backfitting successfully determines the correlations between inputs and outputs and closely matches results from the brute-force analysis, performing the task in orders of magnitude faster. In addition to demonstrating that M1 neurons are good predictors of EMG traces, our work shows that Bayesian Backfitting can be used as a new, statistically sound tool to replace traditional tools in biological data analysis. Such new Bayesian methods enable data analyses that previously could only have been conducted on supercomputing facilities.