An internal model
is a postulated neural process that simulates the response of the motor system in order to estimate the outcome of a motor command.
The internal model theory of motor control argues that the motor system is controlled by the constant interactions of the “plant” and the “controller
In control theory, a controller is a device which monitors and affects the operational conditions of a given dynamical system. The operational conditions are typically referred to as output variables of the system which can be affected by adjusting certain input variables...
.” The plant is the body part being controlled, while the internal model itself is considered part of the controller. Information from the controller, such as information from the CNS, feedback information, and the efference copy
right|thumb|282px|Efference copies are created with our own movement but not those of other people. This is why other people can tickle us but we cannot [[tickle]] ourselves .Efference copy is an internal copy created with a motor command of its predicted movement and its...
, is sent to the plant which moves accordingly.
Internal models can be controlled through either feed-forward
Feed-forward is a term describing an element or pathway within a control system which passes a controlling signal from a source in the control system's external environment, often a command signal from an external operator, to a load elsewhere in its external environment...
Feedback describes the situation when output from an event or phenomenon in the past will influence an occurrence or occurrences of the same Feedback describes the situation when output from (or information about the result of) an event or phenomenon in the past will influence an occurrence or...
control. Feed-forward control computes its input into a system using only the current state and its model of the system. It does not use feedback, so it cannot correct for errors in its control. In feedback control, some of the output of the system can be fed back into the system’s input, and the system is then able to make adjustments or compensate for errors from its desired output. Two primary types of internal models have been proposed: forward models and inverse models. In simulations, models can be combined together to solve more complex movement tasks.
In their simplest form, forward models take the input of a motor command to the “plant” and output a predicted position of the body.
The motor command input to the forward model can be an efference copy, as seen in Figure 1. The output from that forward model, the predicted position of the body, is then compared with the actual position of the body. The actual and predicted position of the body may differ due to noise introduced into the system by either internal (e.g. body sensors are not perfect, sensory noise) or external (e.g. unpredictable forces from outside the body) sources. If the actual and predicted body positions differ, the difference can be fed back as an input into the entire system again so that an adjusted set of motor commands can be formed to create a more accurate movement.
Inverse models use the desired and actual position of the body as inputs to estimate the necessary motor commands which would transform the current position into the desired one. For example, in an arm reaching task, the desired position (or a trajectory of consecutive positions) of the arm is input into the postulated inverse model, and the inverse model generates the motor commands needed to control the arm and bring it into this desired configuration (Figure 2).
Combined forward and inverse models
Theoretical work has showed that in models of motor control, when inverse models are used in combination with a forward model, the efference copy of the motor command output from the inverse model can be used as an input to a forward model for further predictions. For example if, in addition to reaching with the arm, the hand must be controlled to grab an object, an efference copy of the arm motor command can be input into a forward model to estimate the arm's predicted trajectory. With this information, the controller can then generate the appropriate motor command telling the hand to grab the object. It has been proposed that if they exist, this combination of inverse and forward models would allow the CNS to take a desired action (reach with the arm), accurately control the reach and then accurately control the hand to grip an object.
Adaptive Control theory
With the assumption that new models can be acquired and pre-existing models can be updated, the efference copy is important for the adaptive control of a movement task. Throughout the duration of a motor task, an efference copy is fed into a forward model known as a dynamics predictor whose output allows prediction of the motor output. When applying adaptive control theory
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself...
techniques to motor control, efference copy is used in indirect control schemes as the input to the reference model.
A wide range of scientists contribute to progress on the internal model hypothesis. Michael I. Jordan
Michael I. Jordan is a leading researcher in machine learning and artificial intelligence. Jordan was a prime mover behind popularising Bayesian networks in the machine learning community and is known for pointing out links between machine learning and statistics...
, Emmanuel Todorov and Daniel Wolpert contributed significantly to the mathematical formalization. Sandro Mussa-Ivaldi
Ferdinando Mussa-Ivaldi is an Italian born professor at Northwestern University. He is known for his contributions to the fields of motor control, motor learning and computational neuroscience.-Biography:...
, Mitsuo Kawato, Claude Ghez and Reza Shadmehr contributed with numerous behavioral experiments. There is a rich clinical literature on internal models including work from John Krakauer
, Pietro Mazzoni, Maurice A. Smith, Kurt Thoroughman
Kurt A. Thoroughman is an Associate Professor in the Department of Biomedical Engineering at Washington University in St. Louis. He is known for his work in the study of motor control, motor learning, and computational neuroscience....
, Joern Diedrichsen, and Amy Bastian.