Limits...
Current approaches to the study of movement control.

Katsnelson A - PLoS Biol. (2003)

View Article: PubMed Central - PubMed

Affiliation: alla_k@ureach.com <alla_k@ureach.com>

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Almost every sensation we experience or decision we make results in movement... But in order to pet that dog, the brain must take into account an enormous array of information, including the starting position and velocity of the arm, the force required in the fingers to stroke rather than annoy, and the dog's position in space, in order to signal the stimulation of muscle and angling of joints for the necessary movement... And more recently, computational models are being used to simulate simple movement tasks and compare the outcomes with real behaviours and real neural elements, thereby testing ideas of how brain signals are processed to achieve sophisticated motor control... Although the astronauts could see that the ball was not accelerating as it would on Earth, subjects tended to start catching movements too early in zero gravity, reflecting a partial generalization of the effects of gravity... In this month's issue of PLoS Biology, address an apparent contradiction among results of motor-learning experiments... Internal models of both acceleration and velocity show broad generalization in space... This would imply that we do not form an internal model of position... To resolve this issue, the authors examined adaptation to forces that were dependent on both position and velocity of the limb... The results suggest that both position and velocity are encoded in a multiplicative fashion (via a gain field)... The most parsimonious way to view this finding is that neural elements actually encode a direction signal that is modulated by position; such a conclusion is strongly supported by the results of neural recording experiments in motor cortex... As another paper in this month's issue of PLoS Biology shows, signals extracted from the brain can be used directly to control artificial prosthetic devices, which in principle could be adapted to help people with permanent paralysis interact with their environment. recorded multiple signals from the cortex of monkeys trained to perform reaching and grasping tasks... The investigators recorded signals from many brain areas and used several types of empirically derived procedures to extract the necessary signals from the neural-recording data... They demonstrated that multiple cortical areas contain information about hand position, velocity, and other relevant signals, albeit to different degrees.

Show MeSH
Internal Models in the Control of MovementThe brain is hypothesized to use forward models and inverse models to control movement. Both models are subject to change based on errors that are computed by comparing predicted and actual trajectories. (Flowchart adapted from Kawato [1999].)
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC261888&req=5

pbio.0000050-g002: Internal Models in the Control of MovementThe brain is hypothesized to use forward models and inverse models to control movement. Both models are subject to change based on errors that are computed by comparing predicted and actual trajectories. (Flowchart adapted from Kawato [1999].)

Mentions: Internal models themselves are of two types (Figure 2). Forward internal models predict sensory consequences of a planned motor event, and inverse internal models calculate how a movement should be controlled to achieve the desired consequence to essentially transform the desired movement into a motor command based on this calculation. In a review synthesizing a full-scale concept of how multiple internal models might interact with each other, Wolpert et al. (1998) described a complex modularity between different parameters and a computational mechanism that could account for the dynamic interaction between the different modules.


Current approaches to the study of movement control.

Katsnelson A - PLoS Biol. (2003)

Internal Models in the Control of MovementThe brain is hypothesized to use forward models and inverse models to control movement. Both models are subject to change based on errors that are computed by comparing predicted and actual trajectories. (Flowchart adapted from Kawato [1999].)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC261888&req=5

pbio.0000050-g002: Internal Models in the Control of MovementThe brain is hypothesized to use forward models and inverse models to control movement. Both models are subject to change based on errors that are computed by comparing predicted and actual trajectories. (Flowchart adapted from Kawato [1999].)
Mentions: Internal models themselves are of two types (Figure 2). Forward internal models predict sensory consequences of a planned motor event, and inverse internal models calculate how a movement should be controlled to achieve the desired consequence to essentially transform the desired movement into a motor command based on this calculation. In a review synthesizing a full-scale concept of how multiple internal models might interact with each other, Wolpert et al. (1998) described a complex modularity between different parameters and a computational mechanism that could account for the dynamic interaction between the different modules.

View Article: PubMed Central - PubMed

Affiliation: alla_k@ureach.com <alla_k@ureach.com>

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Almost every sensation we experience or decision we make results in movement... But in order to pet that dog, the brain must take into account an enormous array of information, including the starting position and velocity of the arm, the force required in the fingers to stroke rather than annoy, and the dog's position in space, in order to signal the stimulation of muscle and angling of joints for the necessary movement... And more recently, computational models are being used to simulate simple movement tasks and compare the outcomes with real behaviours and real neural elements, thereby testing ideas of how brain signals are processed to achieve sophisticated motor control... Although the astronauts could see that the ball was not accelerating as it would on Earth, subjects tended to start catching movements too early in zero gravity, reflecting a partial generalization of the effects of gravity... In this month's issue of PLoS Biology, address an apparent contradiction among results of motor-learning experiments... Internal models of both acceleration and velocity show broad generalization in space... This would imply that we do not form an internal model of position... To resolve this issue, the authors examined adaptation to forces that were dependent on both position and velocity of the limb... The results suggest that both position and velocity are encoded in a multiplicative fashion (via a gain field)... The most parsimonious way to view this finding is that neural elements actually encode a direction signal that is modulated by position; such a conclusion is strongly supported by the results of neural recording experiments in motor cortex... As another paper in this month's issue of PLoS Biology shows, signals extracted from the brain can be used directly to control artificial prosthetic devices, which in principle could be adapted to help people with permanent paralysis interact with their environment. recorded multiple signals from the cortex of monkeys trained to perform reaching and grasping tasks... The investigators recorded signals from many brain areas and used several types of empirically derived procedures to extract the necessary signals from the neural-recording data... They demonstrated that multiple cortical areas contain information about hand position, velocity, and other relevant signals, albeit to different degrees.

Show MeSH