Kavli Affiliate: Marcelo Mattar
| Authors: Sreejan Kumar, Matthieu B Le Cauchois, Alexander Mathis, Lea Duncker, Jonathon R. Howlett and Marcelo G. Mattar
| Summary:
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.