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Neural Control of Respiration01:18

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Propagation of Action Potentials01:23

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Published on: July 24, 2019

Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models

Thomas Hoellinger1, Mathieu Petieau, Matthieu Duvinage

  • 1Laboratory of Neurophysiology and Movement Biomechanics, CP601, ULB Neuroscience Institute, Université Libre de Bruxelles Brussels, Belgium.

Frontiers in Computational Neuroscience
|June 12, 2013
PubMed
Summary
This summary is machine-generated.

Dynamic recurrent neural networks (DRNNs) effectively model human locomotion coordination by mimicking central pattern generators. This research advances our understanding of motor control and offers potential clinical applications.

Keywords:
biological oscillationscentral pattern generator (CPG)dynamical recurrent neural network (DRNN)human locomotionkinematicsneurophysiology of walking

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Neuroscience
  • Computational Biology
  • Robotics

Background:

  • Motor behavior relies on coordinated neuronal modules across the brain and spinal cord.
  • Human locomotion provides a model system to study the coordination of complex motor patterns.
  • Central pattern generators (CPGs) are neural circuits responsible for rhythmic motor activities like walking.

Purpose of the Study:

  • To model the coordination of human leg movements during locomotion using dynamic recurrent neural networks (DRNNs).
  • To reproduce the planar covariation rule of leg segment elevation.
  • To investigate the biological plausibility and emerging properties of DRNNs as CPG models.

Main Methods:

  • A dynamic recurrent neural network (DRNN) was developed to mimic the oscillatory behavior of human locomotion.
  • The DRNN was trained using sinusoidal signals based on the first three harmonics of leg segment elevation angles.
  • The model's performance was evaluated by comparing its output to biological data and analyzing synaptic weight distributions.

Main Results:

  • The DRNN successfully reproduced the planar covariation rule for leg coordination at various walking speeds.
  • Model accuracy, measured by a similarity index, reached 0.99 with 80 neuronal units.
  • An increasing proportion of inhibitory connections was observed in the DRNN as neuronal units increased, mirroring neurophysiological findings.

Conclusions:

  • DRNNs offer a viable computational model for physiological central pattern generators.
  • This modeling approach can provide insights into basic neuroscience research on motor control.
  • The findings suggest potential for developing clinical applications in rehabilitation and assistive technologies.