Having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using ARMAX/NARMAX models and system identification techniques.
Motor commands of saccades are generally assumed to consist of two parts.
Indeed, studies aimed at identifying the properties of noise in the motor commands of saccades [12], and also of arm movements [19], found that the noise is best characterized as a combination of SDN and constant noise (CN), which is additive noise with a standard deviation independent of the command.
In principle, signals providing movement error information are provided either by sensory feedback about the trajectory and/or endpoint of the on-going movement (sensory error signal) or by feedforward motor commands of fast corrective responses (motor error signal - see for review [1]).
Resonance of a motor program depends on the probabilities of its components (i.e., motor commands of each wrist) and thus, indirectly, on the wrist observations.
This refers to a computational framework for inferring the causes (intentions, goals, and motor commands) of sensory inputs (observed kinematics) by minimizing prediction error at all levels of a cortical hierarchy.
The latency of the N45 response coincides with that involving the conduction of a motor command to the hand muscles and the return of subsequent sensory afferent to the cortex (Tokimura et al. 2000).
As previous studies [11], [18] have suggested, we considered that the generation process of the feedforward motor command consists of two stages.
The rationale for measuring curvature sensitivity during active movement was that PD patients could use information derived from an efference copy of their motor commands to predict the curvedness of their hand trajectories via the use of an internal forward dynamics model [31].
