Article,
Computational approaches to motor control and their potential role for interpreting motor dysfunction
Current Opinion in Neurology, 16 (6): 693-698 (2003)
Abstract
PURPOSE OF REVIEW: Computational frameworks, notably internal models
and optimal control theory, have led to rapid advances in our understanding
of how the brain plans and controls movement. The purpose of this
review is to provide an overview of these theoretical ideas, how
they have been used to interpret motor control, as well as their
potential role for interpreting motor dysfunction. RECENT FINDINGS:
There are two general types of internal models, neural processes
that mimic the mechanical properties of the limb (and environment).
Forward internal models parallel the normal causal flow of the motor
periphery and estimate limb motion from motor commands. Inverse internal
models perform the reverse process by estimating motor commands from
signals related to intended limb motion and/or spatial targets. This
framework has led to several important behavioural observations on
motor planning, control and learning, and has also been influential
for interpreting neural activity in awake, behaving non-human primates.
A more recent framework for interpreting motor function is optimal
control theory, which recognizes that noise or errors are an inherent
feature of the motor system and may influence strategies to plan
and control movement. SUMMARY: Internal models and optimal feedback
control both provide frameworks for interpreting motor performance,
and may be of value for interpreting many motor dysfunctions associated
with neurological injuries. Advanced technologies such as robots
that have play
