The observed shifts in EPDs are usually assumed to reflect some process of adaptation, where the term “adaptation” describe any change in the actual tuning properties of the units, internal model or control strategy, including adaptation to changes in context (Green and Kalaska, 2011 Chase et al., 2012 Fan et al., 2014 Orsborn et al., 2014). ![]() 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 also with the applied forces (Ashe, 1997 Todorov, 2000).ĭuring experiments with brain machine interfaces (BMIs), the estimated PDs (EPDs) of some neurons seem to shift after switching from manual control to brain control (Lebedev et al., 2005 Fan et al., 2014). Detailed investigations suggest that the activity of directionally tuned cortical motor neurons is also modulated by the speed of movement (Moran and Schwartz, 1999). Changes in firing rates with the direction of movement are well described by a cosine function of the angle between movement direction and a neuron-specific direction, dubbed the preferred direction (PD). In particular, center-out reaching experiments indicate that the firing rates of single cortical motor neurons are broadly “tuned” to the direction of movement. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments.įiring 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). ![]() 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. 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. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. Theoretical analysis identifies the conditions for reducing those shifts. 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. 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. Here we address this question in simulations and theoretical analysis. ![]() However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. 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.
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