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Related Concept Videos

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
Somatosensation01:33

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
Somatosensory, Motor, and Association Cortex01:23

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The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at the...
Centroid of a Body: Problem Solving01:03

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The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
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Major Somatic Sensory Pathways01:28

Major Somatic Sensory Pathways

Sensory impulses related to touch, pressure, vibration, and proprioception from various body parts, such as the limbs, trunk, neck, and posterior head, travel to the cerebral cortex through the posterior column-medial lemniscus pathway. The pathway’s name derives from the two white-matter tracts that convey the impulses: the spinal cord's posterior column and the brainstem's medial lemniscus. First-order sensory neurons extend their axons into the spinal cord, forming the posterior columns...
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Related Experiment Video

Updated: May 8, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Neural Representations for Sensorimotor Control. III. Learning a Body-Centered Representation of a Three-Dimensional

F H Guenther, D Bullock, D Greve

    Journal of Cognitive Neuroscience
    |August 22, 2013
    PubMed
    Summary
    This summary is machine-generated.

    The brain learns a 3-D body-centered target position using retinal, eye, and head data. This neural model enables accurate limb movements despite changing spatial relationships.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Robotics

    Background:

    • The brain must create stable spatial representations for accurate motor control.
    • Real-time integration of sensory and motor information is crucial for navigation and interaction.

    Purpose of the Study:

    • To describe a neural model for autonomously learning a body-centered 3-D target position.
    • To explain how this representation facilitates accurate limb movement commands.

    Main Methods:

    • A neural model combining retinal, eye, and head position data.
    • Learning a vector representation (vergence-spherical) of target position.
    • Utilizing opponent processing and corollary discharges for head-centered representation.

    Main Results:

    • The model learns a body-centered representation of 3-D target position.
    • This representation enables accurate visuomotor commands for eye and arm movements.
    • Learning is driven by error signals generated during head movements.

    Conclusions:

    • The proposed neural model demonstrates a plausible mechanism for body-centered spatial representation learning.
    • This framework supports the generation of adaptive and accurate motor commands.
    • The model integrates multimodal sensory information for robust spatial awareness.