Multiplicity of control in the basal ganglia: computational roles of striatal subregions

The basal ganglia, in particular the striatum, are central to theories of behavioral control, and often identified as a seat of action selection. Reinforcement learning (RL) models — which have driven much recent experimental work on this region — cast striatum as a dynamic controller, integrating sensory and motivational information to construct efficient and enriching behavioral policies. Befitting this informationally central role, the BG sit at the nexus of multiple anatomical ‘loops’ of synaptic projections, connecting a wide range of cortical and subcortical structures. Numerous pioneering anatomical studies conducted over the past several decades have meticulously catalogued these loops, and labeled them according to the inferred functions of the connected regions. The specific cotermina of the projections are highly localized to several different subregions of the striatum, leading to the suggestion that these subregions perform complementary but distinct functions. However, until recently, the dominant computational framework outlined only a bipartite, dorsal/ventral, division of striatum. We review recent computational and experimental advances that argue for a more finely fractionated delineation. In particular, experimental data provide extensive insight into unique functions subserved by the dorsomedial striatum (DMS). These functions appear to correspond well with theories of a ‘model-based’ RL subunit, and may also shed light on the suborganization of ventral striatum. Finally, we discuss the limitations of these ideas and how they point the way toward future refinements of neurocomputational theories of striatal function, bringing them into contact with other areas of computational theory and other regions of the brain.

► RL algorithms offer a more finely divided functional framework for striatum. ► Specifically, dorsomedial striatum and accumbens shell together support ‘model-based’ RL. ► RL models of BG are being combined with Bayesian models of perceptual decision-making.