<P> Both open - loop and closed - loop models of optimal control have been studied; the former generally ignores the role of sensory feedback, while the latter attempts to incorporate sensory feedback, which includes delays and uncertainty associated with the sensory systems involved in movement . Open - loop models are simpler but have severe limitations--they model a movement as prerecorded in the nervous system, ignoring sensory feedback, and also fail to model variability between movements with the same task - goal . In both models, the primary difficulty is identifying the cost associated with a movement . A mix of cost variables such as minimum energy expenditure and a "smoothness" function is the most likely choice for a common performance criterion . </P> <P> Bernstein suggested that as humans learn a movement, we first reduce our DOFs by stiffening the musculature in order to have tight control, then gradually "loosen up" and explore the available DOFs as the task becomes more comfortable, and from there find an optimal solution . In terms of optimal control, it has been postulated that the nervous system can learn to find task - specific variables through an optimal control search strategy . It has been shown that adaptation in a visuomotor reaching task becomes optimally tuned so that the cost of movement trajectories decreases over trials . These results suggest that the nervous system is capable of both nonadaptive and adaptive processes of optimal control . Furthermore, these and other results suggest that rather than being a control variable, consistent movement trajectories and velocity profiles are the natural outcome of an adaptive optimal control process . </P> <P> Optimal control is a way of understanding motor control and the motor equivalence problem, but as with most mathematical theories about the nervous system, it has limitations . The theory must have certain information provided before it can make a behavioral prediction: what the costs and rewards of a movement are, what the constraints on the task are, and how state estimation takes place . In essence, the difficulty with optimal control lies in understanding how the nervous system precisely executes a control strategy . Multiple operational time - scales complicate the process, including sensory delays, muscle fatigue, changing of the external environment, and cost - learning . </P> <P> In order to reduce the number of musculoskeletal DOFs upon which the nervous system must operate, it has been proposed that the nervous system controls muscle synergies, or groups of co-activated muscles, rather than individual muscles . Specifically, a muscle synergy has been defined as "a vector specifying a pattern of relative muscle activation; absolute activation of each synergy is thought to be modulated by a single neural command signal ." Multiple muscles are contained within each synergy at fixed ratios of co-activation, and multiple synergies can contain the same muscle . It has been proposed that muscle synergies emerge from an interaction between constraints and properties of the nervous and musculoskeletal systems . This organization may require less computational effort for the nervous system than individual muscle control because fewer synergies are needed to explain a behavior than individual muscles . Furthermore, it has been proposed that synergies themselves may change as behaviors are learned and / or optimized . However, synergies may also be innate to some degree, as suggested by postural responses of humans at very young ages . </P>

Complex systems have low degrees of design freedom