Abstract
Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) 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 vector. Our simulations successfully reproduce apparent shifts in EPDs observed in BMI experiments with different BMI filters, including linear, Kalman and re-calibrated Kalman filters, even with no neural adaptation. Theoretical analysis identifies the conditions for reducing those shifts. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. We conclude that the observed shifts in EPDs may result from experimental conditions, and in particular correlated velocities or tuning weights, even with no adaptation. Under the above assumptions, we show that if neurons are tuned differently to the estimated velocity, estimated position and control signal, the EPD with respect to actual velocity may not capture the real PD in which the neuron encodes the estimated velocity. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments.
Highlights
Firing rates of cortical motor neurons represent a diversity of motor, sensory, and cognitive signals, and most notably the direction and speed of movement (Georgopoulos et al, 1986; Georgopoulos, 2000; Johnson et al, 2001; Paz et al, 2003)
We investigated shifts in estimated preferred direction (EPD) using simulations in which the brain is assumed to implement state estimation and control, and recorded neurons are assumed to encode the relevant signals in a linear way
We demonstrated that the observed shifts in EPDs following the transition to brain control may occur even without any adaptation of the actual tuning weights of the neurons or the internal model
Summary
Firing rates of cortical motor neurons represent a diversity of motor, sensory, and cognitive signals, and most notably the direction and speed of movement (Georgopoulos et al, 1986; Georgopoulos, 2000; Johnson et al, 2001; Paz et al, 2003). Center-out reaching experiments indicate that the firing rates of single cortical motor neurons are broadly “tuned” to the direction of movement. Detailed investigations suggest that the activity of directionally tuned cortical motor neurons is modulated by the speed of movement (Moran and Schwartz, 1999). While the activity of PMd neurons are modulated mainly by the direction and amplitude of the movement (Messier and Kalaska, 2000; Hendrix et al, 2009), the activity of M1 neurons has been shown to correlate with the applied forces (Ashe, 1997; Todorov, 2000).
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