Prefrontal neural geometry of learned cues guides motivated behaviours

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TL;DR

The dorsomedial prefrontal cortex (dmPFC) encodes appetitive and aversive values of learned stimuli, with subpopulations representing valence and salience orthogonally. This neural geometry dynamically shapes motivated behaviors in mice.

Key Takeaways

  • dmPFC populations primarily encode the appetitive and aversive values of learned stimuli.
  • Subpopulations in the dmPFC represent valence and salience along orthogonal information axes.
  • The geometry of dmPFC neuronal representations dynamically guides appetitive and aversive motivated behaviors.

Tags

CortexNeural circuitsNeuroscienceOperant learningPrefrontal cortexScienceHumanities and Social Sciencesmultidisciplinary

Abstract

Animals continuously evaluate their surroundings to decide whether to approach rewarding opportunities or avoid potential threats. Assigning the appropriate importance to environmental stimuli is not only crucial for survival but also underlies complex forms of goal-directed behaviour that are shared across species, including humans1,2,3,4. Understanding how the brain translates such sensory cues into motivated behaviours is, therefore, central to neuroscience and psychology. The dorsomedial prefrontal cortex (dmPFC) is a critical structure that bridges relevant environmental stimuli to goal-directed behaviour. Salience, valence and value are key dimensions defining stimulus relevance, but how the dmPFC processes and organizes such dimensions to drive motivated behaviour remains unclear. Here we monitored single-neuron populations in the dmPFC using calcium imaging in freely moving male mice while discriminating between stimuli predicting different reward or punishment outcomes, which enabled an unprecedented dissociation of salience, valence and value information. We found that dmPFC populations primarily encode appetitive and aversive values of learned stimuli and that subpopulations encode valence and salience along orthogonal information axes. Our results highlight a concurrent multifaceted population coding of value, salience and valence of stimuli during associative learning within dmPFC networks, such that the geometry of dmPFC neuronal representations dynamically shapes appetitive and aversive motivated behaviours.

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Fig. 1: Behavioural framework and task used to evaluate salience, valence and value coding.
Fig. 2: dmPFC neural population activity consistent with value coding scheme.
Fig. 3: Valence and salience are orthogonally represented within dmPFC networks.
Fig. 4: Identifying salience, valence and value ensembles in the dmPFC.

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Data availability

Datasets that support the main findings of this study are available at GitHub (https://github.com/djercog/WinkeEtAl-value-2025).

Code availability

All data analyses were conducted using standard, built-in MATLAB (MathWorks) packages and Python libraries.

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