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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.
Indirect Motor Pathways01:22

Indirect Motor Pathways

The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
The vestibulospinal tract originates in the vestibular nuclei of the brainstem. The vestibular system detects changes in...
Motor Units01:13

Motor Units

The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
Direct Motor Pathways01:11

Direct Motor Pathways

The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and the...

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Related Experiment Video

Updated: Jun 20, 2026

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
04:06

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

Published on: January 12, 2024

Redundancy, self-motion, and motor control.

V Martin1, J P Scholz, G Schöner

  • 1Institut für Neuroinformatik, Ruhr-Universität Bochum NRW 44801, Germany. val33martin@yahoo.de

Neural Computation
|September 1, 2009
PubMed
Summary
This summary is machine-generated.

Human movement control involves selecting among redundant solutions. This study introduces a model where neuronal dynamics generate virtual joint trajectories, explaining how the nervous system controls complex movements and accounting for self-motion in joint velocities.

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Related Experiment Videos

Last Updated: Jun 20, 2026

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
04:06

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

Published on: January 12, 2024

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

Area of Science:

  • Neuroscience
  • Biomechanics
  • Robotics

Background:

  • Human movement often utilizes redundant effector systems, posing a challenge for motor control.
  • Understanding how the nervous system selects movement solutions is key to deciphering motor control principles.

Purpose of the Study:

  • To propose a process model for movement generation in redundant systems.
  • To explain the kinematics of goal-directed pointing movements using a novel neuronal dynamics approach.

Main Methods:

  • Developed a process model incorporating neuronal dynamics and a neuronal timer.
  • Simulated virtual joint trajectories and end-effector motion.
  • Compared model predictions with experimental data from human participants performing pointing tasks.

Main Results:

  • Discovered significant self-motion in joint velocities not directly contributing to end-effector movement.
  • Identified low muscle joint impedance and multiarticulatory muscle coupling as causes of self-motion.
  • Established a correlation between self-motion amount and end-effector path curvature.

Conclusions:

  • The proposed model accurately predicts self-motion and path curvature in human arm movements.
  • Models relying solely on inverse dynamics to cancel interaction torques underestimate self-motion and predict overly straight paths.