Shifts in Estimated Preferred Directions During Simulated BMI Experiments With No Adaptation - Benyamini, Miri; Zacksenhouse, Miriam (Frontiers in Systems Neuroscience, 2021)
Experiments with brain-machine interfaces (BMIs) reveal that the preferred direction (PD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis. Simulations are based on the assumption that the brain implements optimal state estimation and feedback control and that cortical motor neurons encode the estimated state and control signal. Our simulations successfully reproduce the shifts in PD observed in BMI experiments with different BMI filters, including a linear filter and Kalman filters before and after re-calibration, even with no adaptation. Theoretical analysis identifies the conditions under which the PD should not shift after the transition to brain control. We demonstrate that simulations that better satisfy those conditions result in smaller PD shifts. We conclude that the observed PD shifts may result from experimental conditions, and in particular correlated velocities or tuning weights, even when there is no adaptation. Furthermore, we show that under general conditions, the estimated PD may not capture the real PD of the neuron. Our investigation provides theoretical and simulation tools for better understanding shifts in PD and BMI experiments.