Population-level coding of avoidance learning in medial prefrontal cortex
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Date
2024-09
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Journal Article
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yes
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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.
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published
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27 (9)
Pages / Article No.
1805 - 1815
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Organisational unit
09479 - Grewe, Benjamin / Grewe, Benjamin
09776 - Mante, Valerio / Mante, Valerio
Notes
Funding
189251 - Ultra compact miniaturized microscopes to image meso-scale brain activity (SNF)
173721 - Temporal Information Integration in Neural Networks (SNF)
173721 - Temporal Information Integration in Neural Networks (SNF)
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