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Updated: Feb 24, 2026

Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior
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Continuous input drives motor cortical dynamics during reaching.

Andrew Schwartz1, Hongwei Mao1, Brady Hasse1,2,3

  • 1Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.

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Summary
This summary is machine-generated.

External input, not internal processing, drives motor cortex activity during reaching. This finding challenges existing models by showing how continuous external signals orchestrate neural state transitions for motor control.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Current models propose autonomous internal processing drives motor cortical activity.
  • The role of continuous external input in shaping neural dynamics during behavior remains less understood.

Purpose of the Study:

  • To investigate the influence of continuous external input versus intrinsic processing on motor cortical neuron activity during reaching.
  • To determine the primary drivers of state transitions observed in neural populations during motor tasks.

Main Methods:

  • Utilized hybrid neural networks (HNNs) combining artificial connectivity with empirical firing rates.
  • Recorded neural population activity during a reaching task.
  • Employed HNNs to model neural systems and test hypotheses about input sources.

Main Results:

  • Observed distinct state transitions in motor cortical neuron firing rates during reaching.
  • HNNs accurately reproduced recorded firing rates and population state transitions.
  • Demonstrated that extrinsic, episodically modulated input, rather than intrinsic processing, was responsible for the observed neural dynamics.
  • Identified that episodic external drive alters pre-threshold input integration statistics, causing state transitions.

Conclusions:

  • Continuous external input is a key driver of dynamic activity and state transitions in the motor cortex during reaching.
  • Findings challenge the primacy of intrinsic action in current models of cortical function.
  • Hybrid neural networks provide a powerful tool for dissecting the contributions of intrinsic and extrinsic factors in neural systems.