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Neural Algorithms and Circuits for Motor Planning.

Hidehiko K Inagaki¹, Susu Chen², Kayvon Daie^2,3

¹Max Planck Florida Institute for Neuroscience, Jupiter, Florida, USA;

Annual Review of Neuroscience

|March 22, 2022

Summary

This summary is machine-generated.

Neural population activity in the brain prepares and executes movements. Cortical dynamics during motor planning involve neural attractors, reshaped by subcortical signals for action commitment and execution.

Keywords:

ALM dimensionality reduction licking mice motor control motor cortex

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Area of Science:

Neuroscience
Computational Neuroscience
Motor Control

Background:

The brain's ability to plan and execute volitional movements relies on complex neural population activity.
Understanding the slow dynamics of movement preparation and fast dynamics of movement initiation is crucial.
Previous studies explored these dynamics in eye, limb, tongue, and head movements across species.

Purpose of the Study:

To investigate how neural networks generate distinct slow and fast dynamics for movement preparation and execution.
To test dynamical systems models using perturbations of neural activity.
To elucidate the role of cortical attractors and subcortical control signals in motor control.

Main Methods:

Utilized rapid and calibrated perturbations of neural activity in rodent models.
Employed dynamical systems modeling to analyze neural population activity.
Integrated experimental data with computational approaches.

Main Results:

Cortical dynamics during motor planning exhibit neural activity patterns resembling fixed points, termed attractors.
Subcortical control signals dynamically reshape and shift these attractors over various timescales.
These mechanisms facilitate action commitment and enable rapid transitions to movement execution.

Conclusions:

Motor planning and execution involve a sophisticated interplay between cortical attractors and subcortical modulation.
Dynamical systems models provide a framework for understanding neural control of movement.
Ongoing research in rodents aims to uncover the brain-wide circuit implementation of these motor control algorithms.