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Related Concept Videos

Motor Units00:46

Motor Units

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A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
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Motor Units01:13

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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.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
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Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
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Model for a flexible motor memory based on a self-active recurrent neural network.

Kim Joris Boström1, Heiko Wagner, Markus Prieske

  • 1Motion Science, University of Münster, Horstmarer Landweg 62b, 48149 Münster, Germany; Center for Nonlinear Science (CeNoS), 48149 Münster, Germany.

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|October 15, 2013
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Summary

This study introduces a flexible motor memory model using recurrent neural networks. It demonstrates how neural networks can store and flexibly modulate movement patterns, inspired by brain activity.

Keywords:
233023434160Motor controlMotor learningMotor memoryNeural networksReservoir computing

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Area of Science:

  • Computational Neuroscience
  • Robotics
  • Motor Control

Background:

  • Flexible motor memory is crucial for fluent skill execution.
  • Existing models often lack continuous modulation capabilities.
  • Brain exhibits resting-state activity and neural outsourcing.

Purpose of the Study:

  • To propose and simulate a flexible motor memory model.
  • To investigate continuous modulation of stored movement patterns.
  • To explore the concept of neural outsourcing in motor control.

Main Methods:

  • Utilized a recurrent network architecture based on reservoir computing.
  • Simulated a neural network with a thousand neurons.
  • Trained the network using experimental muscular activation and kinetic feedback data.

Main Results:

  • The model successfully stored and retrieved elementary movement patterns.
  • Demonstrated continuous modulation via linear inter- and extrapolation.
  • Showcased self-active network maintaining recurrent flow, mimicking brain resting-state activity.
  • Illustrated neural outsourcing by shifting computational load to lower-level structures.

Conclusions:

  • The proposed model offers a flexible and continuously modulating motor memory.
  • Neural outsourcing may explain fluent, attention-independent skill execution.
  • The model provides insights into biological motor control mechanisms.