Population-level coding of avoidance learning in medial prefrontal cortex

Nat Neurosci. 2024 Sep;27(9):1805-1815. doi: 10.1038/s41593-024-01704-5. Epub 2024 Jul 29.

Abstract

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a new tone-signaled active avoidance paradigm in mice. We developed an analysis approach based on dimensionality reduction and decoding that allowed us to identify interpretable task-related population activity patterns. While a large fraction of tone-evoked activity was not informative about behavior execution, we identified an activity pattern that was predictive of tone-induced avoidance actions and did not occur for spontaneous actions with similar motion kinematics. Moreover, this avoidance-specific activity differed between distinct avoidance actions learned in two consecutive tasks. Overall, our results are consistent with a model in which mPFC contributes to the selection of goal-directed actions by transforming sensory inputs into specific behavioral outputs through distributed population-level computations.

MeSH terms

  • Animals
  • Avoidance Learning* / physiology
  • Male
  • Mice
  • Mice, Inbred C57BL
  • Neurons / physiology
  • Prefrontal Cortex* / physiology