Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system.

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error.

Evolution, biomechanics, and neurobiology converge to explain selective finger motor control.

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements.

Peripheral artery disease causes consistent gait irregularities regardless of the location of leg claudication pain.

Background: The most common symptom of peripheral artery disease (PAD) is intermittent claudication that involves the calf, thigh, and/or buttock muscles. How the specific location of this leg pain is related to altered gait, however, is unknown.

Objectives: We hypothesized that because the location of claudication symptoms uniquely affects different leg muscle groups in people with PAD, this would produce distinctive walking patterns.