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Forward model of an arm movement. The motor command, u(t), of the arm movement is input to the plant and the predicted position of the body, x̃(t), is output.

An internal model is a neural process that simulates the response of the motor system in order to estimate the outcome of a motor command.

The internal model account of motor control argues that the motor system is controlled by the constant interactions of the “plant” and the “controller.” 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, is sent to the plant which moves accordingly.

Internal models can be controlled through either feed-forward or feedback 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 exist: forward models and inverse models. These models can be combined together to solve more complex movement tasks.


Forward models

Figure 1. The desired position of the body is the reference input to the controller, which generates the necessary motor command. This motor command is sent to the plant to move the body and an efference copy of the motor command is sent to a forward model. The output from the forward model (predicted body position) is compared with the output from the plant (body position). Noise from the system or the environment may cause differences between the actual and predicted body positions. The error (difference) between the actual and predicted positions can provide feedback to improve the movement for the next iteration of the internal model.

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

Figure 2. Inverse model of a reaching task. The arm’s desired trajectory, Xref(t), is input into the model, which generates the necessary motor commands, ũ(t), to control the arm.

Inverse models use the desired or actual position of the body as the input to then determine or identify the necessary motor commands. For example, in a reaching task, the desired trajectory of the arm is input into the inverse model, and the motor commands to control the arm are output (Figure 2).

Combined forward and inverse models

When 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. 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. This combination of inverse and forward models allows 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.[1]

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 techniques to motor control, efference copy is used in indirect control schemes as the input to the reference model.


  1. ^ Kawato, M (1999). "Internal models for motor control and trajectory planning". Current Opinion in Neurobiology 9: 718–727. doi:10.1016/S0959-4388(99)00028-8.  


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