Institute for Mathematical Behavioral Sciences

When learning a new skill, it is vital that we also learn the context in which that skill is relevant. In this talk I will describe a neurocomputational theory of how such context learning is mediated in the brain. Skill learning is known to depend on a major subcortical structure called the striatum. The new theory proposes that a key component of context learning during skill acquisition is provided by cholinergic interneurons in the striatum known as TANs (i.e., Tonically Active Neurons). Evidence suggests that the TANs exert a tonic inhibitory influence over striatal output neurons that prevents the execution of any striatal-dependent action. The TANs learn to pause to rewarding contexts, and this pause releases the striatal output neurons from inhibition, thereby facilitating the learning and expression of striatal-dependent behaviors. When the context changes, the TANs cease to pause, thereby protecting striatal learning from decay in non-rewarding environments. In the computational version of this theory, neural units in the relevant brain regions are each modeled by two coupled differential equations ¬ one that models fast changes in membrane potential and a second that models slow changes in the activation and inactivation of various intracellular ion channels. Learning is modeled via a biologically detailed form of reinforcement learning. The model accounts for some key single-cell recording and behavioral results. For example, the model accounts for a number of well-known learning phenomena (e.g., fast reacquisition following extinction, spontaneous recovery), and offers new interpretations of some classic societal problems (e.g., why bad habits are so difficult to break; why recidivism from drug-dependency treatment programs is so high).