Motor control

Motor control are information processing related activities carried out by the central nervous system that organize the musculoskeletal system to create coordinated movements and skilled actions. Motor control is also the name of a thriving field within Neuroscience that analyzes how people, animals and their nervous system controls movement.

Simple tasks such as reaching for a cup of coffee are actually surprisingly complex. They arise from a complex coordination between:
 * muscles
 * limbs
 * neural circuitry

The involved processes are complex and may be roughly divided as follows:
 * perception
 * motor planning
 * motor execution
 * feedback
 * biomechanics

Motor control is the process that must be performed in order to achieve movement. In other words, motor coordination is essentially the complex set of interactions between neural processes involved in moving a limb, and the actual limb in movement.

Aspects of Motor Control
Motor control can be thought to concern two types of movements: volitional and reflexive.

Beyond anatomical divisions, motor coordination studies often seek to explore one of the following questions:


 * What are the physics and mathematical modeling of the limb movement involved?
 * How complicated and coordinated is the limb movement? How are movements of several joints coordinated?

Fortunately for researchers, multi-limb movements can often be modeled by simple mathematical models. A single limb can be broken down into components such as muscles, tendons, bones, and nerves. The physics are then derived with the aid of modern computers. The study of multi-limb movement is then only slightly more complicated. The development of elementary models of intelligence, along with a gambit of built-in reflexive reactions, is suited to the modeling of this system.

Theoretical frameworks about motor control

 * Coordination Dynamics framework emphasizes the dynamical and time-continuous interplay between brain, body, and environment as a holistic system.
 * Equilibrium point approaches emphasize that biomechanics and in particular the elastic properties of muscles and reflexes in the spinal cord can render many movement problems easy.
 * Reinforcement learning based approaches emphasize the learning of movement from motor errors.
 * Optimal control and estimation frameworks (see Bayesian brain) start from the computational problems that need to be solved and ask which solutions would be optimal. Many internal model studies fall into this framework.