Compressed Cortical Input Separates Control from Dynamics in Striatum

Abstract

The dorsolateral striatum (DLS) supports diverse time-sensitive behaviors--action chunking, duration estimation, and motor timing--yet no single framework is able to explain all of these phenomena. Here, we propose that the massive convergence of cortical projections onto the striatum provides such a framework. We develop a corticostriatal neural network model in which a recurrent cortical module communicates with a recurrent striatal module through a low-dimensional, noisy bottleneck, with the whole system trained via reinforcement learning. Across three DLS-associated tasks, compression produces a consistent computational motif whereby cortex provides low-dimensional control signals while the striatum generates stable, time-encoding dynamics. This separation gives rise to chunking behavior with action slipping, intensity-biased duration judgements with stimulus-modulated time coding, and stereotyped motor timing programs. Perturbation of the compressed cortical signal causally shifts behavior while preserving sequential structure in striatum activity. In sum, our results unify information-theoretic and dynamical systems perspectives of basal ganglia function to link anatomical compression to a variety of temporally-sensitive sensorimotor behaviors implicated in the DLS.