Biology subjects

Godinho, B. S.

Publications and source records attributed to Godinho, B. S..

2 recordsLinked to original sources

bioRxiv2026

Structured and flexible representations in medial-frontal cortex support goal-directed navigation

Humans and animals plan actions to achieve goals in worlds that are complex and continually changing. While planning is critically dependent on the prefrontal cortex in humans, little is known about its cellular underpinnings. Mechanistic understanding has been limited by a scarcity of controlled animal experiments in which subjects must flexibly plan novel behaviours on every trial. Here we characterise the neural representations and dynamics of mouse medial frontal cortex (mFC) during flexible navigation in structured environments. We trained mice to navigate complex mazes, to goals that changed location on every trial. Optogenetic silencing established that mFC was necessary for efficient navigation. mFC activity was dominated by two factorised components: (i) a structured representation of subjects position within the maze that formed an efficient code for behavioural trajectories, and (ii) a flexible representation of the shortest path-distance to the current goal. Both representations oscillated within local field potential (LFP) theta cycles, processing from further to closer to the goal at a systematic offset. These data suggest a computation in which mFC evaluates possible futures by their distance-to-goal to update a structured behavioural policy.

neuroscience↗

bioRxiv2026

Flexible route planning and rapid structure learning by mice in complex environments

Action selection using predictive models of the environment plays a fundamental role in human and animal behaviour, yet is poorly understood at circuit and algorithmic levels. Spatial navigation is an attractive domain for characterising how world models guide action selection. However spatial behaviours are shaped by multiple control systems including habits, vector-navigation using a Euclidean model of spatial relationships, and route planning using models of environment structure. Understanding how world models support navigation requires assays that dissociate control systems and decorrelate behavioural variables, while generating large datasets that allow precise quantification of brain-behaviour relationships. Here we developed and computationally optimised a behavioural assay to quantify flexible navigation using knowledge of environment structure. Mice navigated to visually cued goals in complex mazes, with randomised start and goal locations on each trial, generating thousands of non-repetitive goal-directed navigation trajectories. They navigated efficiently - strongly favouring options on the shortest path-to-goal, and learnt rapidly - demonstrating knowledge of maze structure from their first sessions in new environments. We anticipate the assay will be useful for characterising how world models support flexible behaviour.

animal behavior and cognition↗