Boeshertz, G. | bioRxiv Science

bioRxiv2025

A frontal motor circuit for economic decisions and actions

Flexible behaviour requires transforming abstract cognitive representations, such as value preferences, into concrete motor actions. During economic decision-making, individuals evaluate options to guide choices and then transform these choices into specific actions to obtain rewards. Understanding how neural circuits convert these abstract economic decisions into spatial actions remains challenging because decision formation and motor planning are typically intertwined. Here we introduce a mouse task that temporally dissociates value-guided decisions from spatial action planning, and show that a frontal motor network implements the transformation across decision stages through dynamic circuit reconfiguration. Using cortex-wide imaging and optogenetic perturbations, we identified a frontal motor circuit that was causally required for both abstract and motor stages of choice. During the abstract decision stage, neurons in this circuit encoded option values and economic choices independently of sensorimotor contingencies, and unilateral silencing impaired decisions without spatial bias. In contrast, during spatial planning, value and spatial signals were non-linearly integrated to guide action selection, and unilateral silencing produced an ipsilateral bias. A dynamical model captured this transformation, predicting a mode switch from cooperative interhemispheric maintenance of economic choice to competitive, lateralized control of actions, which we validated with simultaneous bilateral recordings. These findings demonstrate how frontal motor circuits reconfigure their interactions to bridge abstract cognition and concrete actions, providing a circuit-level mechanism for flexible, value-guided behaviour.

neuroscience↗

bioRxiv2025

Predictive learning enables compositional representations

The brain builds predictive models to plan future actions. These models generalize remarkably well to new environments, but it is unclear how neural circuits acquire this flexibility. Compositional representations, which have been observed in the brain, could explain this adaptability. They enable a process called compositional generalization, where independent modules performing different computations can be selected to perform novel composite tasks. In this work, we show that compositional representations emerge in recurrent neural networks (RNNs) trained solely to predict future sensory inputs. We trained an RNN to predict frames in a visual environment defined by independent latent factors and their corresponding dynamics. We found that the network learned to solve this task by developing a compositional model. Specifically, it had disentangled representations of the latent factors, and formed distinct, modular clusters, each implementing a single dynamic. The network autonomously selected which cluster to use according to the sensory inputs, without task labels, using competitive dynamics between clusters. This modular and disentangled architecture enabled the network to perform compositional generalization, accurately predicting outcomes in novel contexts composed of unseen combinations of dynamics. Our findings explain how an unsupervised mechanism can learn the modular causal structure of an environment in a compositional code.

neuroscience↗