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Updated: Jul 5, 2025

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Downstream network transformations dissociate neural activity from causal functional contributions.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neuroscientists use neural activity patterns to understand brain function and behavior.
  • The reliability of inferring causal contributions from observed neural activity is unclear.

Purpose of the Study:

  • To develop a framework for assessing time-varying causal contributions of neural elements.
  • To investigate the relationship between neural activity and causal roles in collective outcomes.

Main Methods:

  • Developed a systematic multi-site perturbation framework.
  • Applied the framework to toy examples and artificial neural networks.

Main Results:

  • Recorded neural activity patterns may not accurately reflect causal contributions due to network transformations.
  • Demonstrated limitations in inferring causality solely from activity data.

Conclusions:

  • Observed neural activity is not always informative of a neural unit's causal contribution.
  • The proposed framework aids in elucidating causal neural contributions through rigorous perturbation analysis.