ePoster

DORSOMEDIAL FRONTAL CORTEX REPRESENTATIONS OF STRATEGY-DEPENDENT VISUAL DECISIONS

Yuliia Shevchukand 5 co-authors

University of Oxford

FENS Forum 2026 (2026)
Barcelona, Spain
Board PS02-07PM-096

Presentation

Date TBA

Board: PS02-07PM-096

Poster preview

DORSOMEDIAL FRONTAL CORTEX REPRESENTATIONS OF STRATEGY-DEPENDENT VISUAL DECISIONS poster preview

Event Information

Poster Board

PS02-07PM-096

Abstract

Animals discover more efficient decision-making strategies across different stages of learning. Frontal cortical neuronal activity plays a critical role in defining these strategies. However, it remains unclear how frontal cortical circuits represent distinct strategies across learning. To address this question, we used two-photon imaging to record neuronal signals across the dorsomedial frontal cortex (dmFC) while mice performed a visual decision-making task. Head-fixed mice reported the position of a stimulus with varying contrast appearing on the left or right side of the screen, and received a reward for correct choices. Based on psychometric curves, we identified two different learning strategies: one-sided strategy and balanced strategy, consistent with our previous findings (Liebana et al., Cell, 2025). The one-sided strategy, usually observed in earlier sessions, marked by animals associating a stimulus on only one side of the screen with a choice (and choosing the alternative choice in trials where the associated stimulus was not presented). The balanced strategy, emerging in later sessions, marked by animals associating stimuli on both sides with their respective choices. Our analyses so far indicate that dmFC population activity reflects these decision-making strategies, apparent in the tuning curves of stimulus-evoked dmFC population neuronal responses. The preliminary results suggest that dmFC exhibits neuronal responses that vary across different stages of learning, corresponding to distinct strategies used across learning. This behavioral transition provides a framework for examining how cortical population codes reorganize as learning progresses.

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