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Invariant neural dynamics drive commands to control different movements.

Vivek R Athalye1, Preeya Khanna2, Suraj Gowda3

  • 1Zuckerman Mind Brain Behavior Institute, Departments of Neuroscience and Neurology, Columbia University, New York, NY 10027, USA.

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Summary

The nervous system reuses invariant neural dynamics to generate diverse movements. These dynamics guide brain-machine interface commands, enabling flexible and generalizable motor control.

Keywords:
brain-machine interfacesdynamical systemsmotor commandsmotor controlmotor cortexmovement representationsneural population dynamicsneuroprostheticsoptimal feedback control

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Area of Science:

  • Neuroscience
  • Motor Control
  • Computational Neuroscience

Background:

  • The nervous system's ability to produce varied movements may stem from reusing invariant neural codes.
  • Previous research observed similar neural population activity dynamics across different movements.

Purpose of the Study:

  • To investigate if invariant neural population dynamics are actively used to generate motor commands.
  • To determine how these dynamics relate to the commands issued for movement control.

Main Methods:

  • Utilized a brain-machine interface (BMI) to decode motor cortex activity in rhesus macaques.
  • Transformed neural activity into commands for a neuroprosthetic cursor.
  • Analyzed neural activity patterns and their temporal dynamics during different movements.

Main Results:

  • Identified that distinct neural activity patterns can produce the same motor command.
  • Discovered that transitions between neural activity patterns follow consistent, invariant dynamics across movements.
  • Demonstrated that these low-dimensional invariant dynamics align with the BMI, predicting command-relevant neural activity.

Conclusions:

  • Invariant neural dynamics are crucial for generating a variety of movements.
  • These dynamics facilitate the transformation of feedback into generalizable motor commands.
  • A model of optimal feedback control (OFC) supports the role of invariant dynamics in reducing neural computational load.