Learning reweights the decision dynamics of cortico-basal ganglia-thalamic pathways from deliberation to commitment

Mammals flexibly adjust their decision strategies in dynamic environments based on prior experience. The cortico-basal ganglia-thalamic (CBGT) circuit is recognized as a critical driver of this adaptability, yet how plasticity-induced modifications to CBGT dynamics translate into modifications of decision policies remains poorly understood. Here we simulate learning in a biologically-grounded spiking CBGT model that learns to select a rewarded action via dopamine-dependent plasticity at corticostriatal synapses. Relying on three previously identified control ensembles (responsiveness, pliancy, and choice) within CBGT circuits, we disentangle the distinct roles these subnetworks have in reshaping decision trajectories across learning. Control ensemble dynamics were mapped onto the evolution of evidence accumulation, revealing within-trial parameter adjustments that shape decision dynamics and outcomes. Our results emphasize that learning optimizes not only what choice is favored, but also how the phases within the decision unfold. Early in the decision, learning accelerates evidence accumulation by driving the activity of corticothalamic and direct pathways. During later deliberation, this drive is temporarily restrained, and the indirect and pallidostriatal pathways become more critical for maintaining decision thresholds, preventing premature commitment despite the increased choice bias. As the system approaches the decision point, the direct pathway regains dominance, triggering boundary collapse to facilitate action selection. This mechanism effectively shifts decisions from deliberative to committed reward-directed choices, improving both speed and accuracy while preserving system stability and control throughout the process. Author summaryWe investigate how specific subnetworks within cortico-basal ganglia-thalamic (CBGT) circuits reshape decision-making through dopamine-dependent plasticity at corticostriatal synapses. Using simulations of a simple two-choice task with a single rewarded target, we identify phase-specific reconfigurations of subnetwork activity that emerge over the course of learning. We then link these circuit-level changes to an algorithmic description of behavior by mapping them onto decision policies within a dynamic evidence accumulation framework. This analysis reveals a learning-driven shift from deliberative to more committed decision strategies, achieved through flexible coordination of decision boundaries and evidence accumulation rates. Together, these adjustments optimize decision speed and accuracy while preserving caution during ongoing choices. Our findings provide mechanistic insight into how plasticity within CBGT circuits supports adaptive decision-making across different levels of uncertainty and task demands.