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Related Concept Videos

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

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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.
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Control Systems01:10

Control Systems

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Time-Domain Interpretation of PD Control01:07

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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Muscle Coordination and Action01:24

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Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
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Linear Momentum in Control Volume01:13

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Newton's second law is applied to obtain the linear momentum in a control volume in a fluid system. According to this law, the rate of change of linear momentum is equal to the sum of external forces acting on the system. When a control volume matches the fluid system at a specific moment, the forces acting on both are identical. Reynolds transport theorem helps explain this by breaking down the system's linear momentum into two components: the rate of change of linear momentum within...
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Dynamics of Circular Motion01:30

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An object undergoing circular motion, like a race car, is accelerating because it is changing the direction of its velocity. This centrally directed acceleration is called centripetal acceleration. This acceleration acts along the radius of the curved path (thus is also referred to as radial acceleration).
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Related Experiment Video

Updated: Apr 28, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Bringing the dynamics of movement under control.

Alfonso Renart1

  • 1Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal.

Neuron
|June 20, 2014
PubMed
Summary

Neural network learning stabilizes spontaneous activity, reproducing key aspects of movement preparation and execution. This research sheds light on the mechanisms of neural dynamics during movement.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • The precise mechanisms governing the dynamics of neural activity related to movement remain largely unknown.
  • Understanding these mechanisms is crucial for deciphering motor control and planning.

Purpose of the Study:

  • To investigate the role of recurrent neural networks in generating movement-related neural activity.
  • To determine if learning-induced stabilization of spontaneous activity can explain observed neural dynamics during movement.

Main Methods:

  • Utilized computational modeling of recurrent neural networks.
  • Simulated network activity dynamics and analyzed spontaneous, preparatory, and movement-related activity patterns.

Main Results:

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Related Experiment Videos

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  • A recurrent network model with learning-stabilized spontaneous activity successfully reproduced key features of neural activity observed during movement preparation and execution.
  • The model demonstrated that stabilizing network dynamics through learning can account for complex neural patterns.

Conclusions:

  • Learning-induced stabilization of spontaneous activity in recurrent networks is a plausible mechanism underlying movement-related neural dynamics.
  • This finding offers a novel computational framework for understanding neural control of movement.