Most behaviors unfold in time and include a sequence of submovements or cognitive activities. In addition, most behaviors are automatic and repeated daily throughout life. Yet, relatively little is known about the neurobiology of automatic sequence production. Past research suggests a gradual transfer from the associative striatum to the sensorimotor striatum, but a number of more recent studies challenge this role of the BG in automatic sequence production. In this article, we propose a new neurocomputational model of automatic sequence production in which the main role of the BG is to train cortical–cortical connections within the premotor areas that are responsible for automatic sequence production. The new model is used to simulate four different data sets from human and nonhuman animals, including (1) behavioral data (e.g., RTs), (2) electrophysiology data (e.g., single-neuron recordings), (3) macrostructure data (e.g., TMS), and (4) neurological circuit data (e.g., inactivation studies). We conclude with a comparison of the new model with existing models of automatic sequence production and discuss a possible new role for the BG in automaticity and its implication for Parkinson's disease.

An essential component of skill acquisition is learning the environmental conditions in which that skill is relevant. This article proposes and tests a neurobiologically detailed theory of how such learning is mediated. The theory assumes that a key component of this learning is provided by the cholinergic interneurons in the striatum known as tonically active neurons (TANs). The TANs are assumed to exert a tonic inhibitory influence over cortical inputs to the striatum that prevents the execution of any striatal-dependent actions. The TANs learn to pause in rewarding environments, and this pause releases the striatal output neurons from this inhibitory effect, thereby facilitating the learning and expression of striatal-dependent behaviors. When rewards are no longer available, the TANs cease to pause, which protects striatal learning from decay. A computational version of this theory accounts for a variety of single-cell recording data and some classic behavioral phenomena, including fast reacquisition after extinction.

Many studies suggest that the sustained activation underlying working memory (WM) maintenance is mediated by a distributed network that includes the prefrontal cortex and other structures (e.g., posterior parietal cortex, thalamus, globus pallidus, and the caudate nucleus). A computational model of WM, called FROST (short for FROntal-Striatal-Thalamic), is proposed in which the representation of items and spatial positions is encoded in the lateral prefrontal cortex. During delay intervals, activation in these prefrontal cells is sustained via parallel, prefrontal cortical-thalamic loops. Activation reverberates in these loops because prefrontal cortical excitation of the head of the caudate nucleus leads to disinhibition of the thalamus (via the globus pallidus). FROST successfully accounts for a wide variety of WM data, including single-cell recording data and human behavioral data.