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Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model.

Lorenzo Vannucci1, Egidio Falotico1, Cecilia Laschi1

  • 1The BioRobotics Institute, Scuola Superiore Sant'AnnaPontedera, Italy.

Frontiers in Neuroscience
|June 30, 2017
PubMed
Summary

This study introduces a novel spike-based model for translating proprioceptive feedback using muscle spindle afferent fibers. This bio-inspired mechanism enables real-time robotic control and neuroscience research.

Keywords:
muscle spindleneuromorphic hardwareneuromorphic sensingneuroroboticsproprioceptive sensors

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Area of Science:

  • Computational Neuroscience
  • Robotics
  • Biologically Inspired Engineering

Background:

  • Connecting neural simulations with physical systems aids robotics and neuroscience.
  • Proprioceptive feedback is crucial for motor control and action-perception loops.

Purpose of the Study:

  • To develop a fully spike-based, biologically inspired mechanism for proprioceptive feedback translation.
  • To model neural activity of muscle spindle afferent fibers (Type Ia and Type II).
  • To incorporate static and dynamic gamma-motoneuron activity.

Main Methods:

  • Implemented a computational model of Type Ia and Type II muscle spindle afferent fibers.
  • Simulated muscle spindle contraction/relaxation using proprioceptive sensor data (motor encoders).
  • Deployed the model on NEST simulator and SpiNNaker neuromorphic hardware.

Main Results:

  • Successfully translated encoder values into biologically plausible neural activity.
  • Demonstrated feasibility in simulated and real-time robotic applications.
  • Validated the model on both simulated and physical robotic platforms.

Conclusions:

  • The model serves as a spike-based building block for neuromorphic platforms.
  • Enables the development of sensory-motor closed loops for robotics and neuroscience.
  • Facilitates neural simulations of central nervous system areas and low-level reflexes.