Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning

Colin Bredenberg, Cristina Savin, Roozbeh Kiani

Research output: Contribution to journalArticlepeer-review


Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

Original languageEnglish (US)
Article numbere1635232024
JournalJournal of Neuroscience
Issue number16
StatePublished - Apr 17 2024


  • circuit perturbation
  • compensation
  • functional integrity
  • re-learning
  • recurrent neural networks
  • redundant architecture

ASJC Scopus subject areas

  • General Neuroscience


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