Information processing, dimensionality reduction and reinforcement learning in the basal ganglia

Abstract

Modeling of the basal ganglia has played a major role in our understanding of this elusive group of nuclei. Models of the basal ganglia have undergone evolutionary and revolutionary changes over the last 20 years, as new research in the fields of anatomy, physiology and biochemistry of these nuclei has yielded new information. Early models dealt with a single pathway through the nuclei and focused on the nature of the processing performed within it, convergence of information versus parallel processing of information. Later, the Albin-DeLong "box-and-arrow" model characterized the inter-nuclei interaction as multiple pathways while maintaining a simplistic scalar representation of the nuclei themselves. This model made a breakthrough by providing key insights into the behavior of these nuclei in hypo- and hyper-kinetic movement disorders. The next generation of models elaborated the intra-nuclei interactions and focused on the role of the basal ganglia in action selection and sequence generation which form the most current consensus regarding basal ganglia function in both normal and pathological conditions. However, new findings challenge these models and point to a different neural network approach to information processing in the basal ganglia. Here, we take an in-depth look at the reinforcement driven dimensionality reduction (RDDR) model which postulates that the basal ganglia compress cortical information according to a reinforcement signal using optimal extraction methods. The model provides new insights and experimental predictions on the computational capacity of the basal ganglia and their role in health and disease.