Cortical control of motor learning

The execution of the simplest gestures requires the accurate coordination of several muscles. In robotic systems, engineers coordinate the action of multiple motors by writing computer code that specifies how the motors must be activated for achieving the desired robot motion and for compensating for unexpected disturbance. Humans and animals follow another path. Something akin to programming is achieved in nature by the biological mechanisms of synaptic plasticity; that is, by the variation in efficacy of neural transmission brought about by past history of pre- and postsynaptic signals. However, robots and animals differ in another important way. Robots (at least those of the current generations) have fixed mechanical structure and dimensions. In contrast, the mechanics of muscles, bones, and ligaments change over time: the length of our limbs varies as we grow into adulthood; some part of our body may lose its functionality following a lesion or a degenerative process; muscle mechanics may vary over just a few minutes of intense activity. Because of these changes, the central nervous system must continuously adapt motor commands to the mechanics of the body. Adaptation - the ability to carry previously learnedmotor skills into new mechanical contexts - is a form of motor learning. In this chapter, we present a view of motor learning that starts from the analysis of the computational problems associated with the execution of the simplest gestures. We discuss the theoretical idea of internal models and present some evidence and theoretical considerations suggesting that internal models of limb dynamics may be obtained by the combination of simple modules or “motor primitives.” Then, we review some experimental results on the activity of neurons in the cortex during a learning task. These findings suggest that the motor cortical areas include neurons that process well-acquired movements as well as neurons that change their behavior during and after being exposed to a new task.