A model of reaching that integrates reinforcement learning and population encoding of postures

When monkeys tackle novel complex behavioral tasks by trial-and-error they select actions from repertoires of sensorimotor primitives that allow them to search solutions in a space which is coarser than the space of fine movements. Neuroscientific findings suggested that upper-limb sensorimotor primitives might be encoded, in terms of the final goal-postures they pursue, in premotor cortex. A previous work by the authors reproduced these results in a model based on the idea that cortical pathways learn sensorimotor primitives while basal ganglia learn to assemble and trigger them to pursue complex re-ward-based goals. This paper extends that model in several directions: a) it uses a Kohonen network to create a neural map with population encoding of postural primitives; b) it proposes an actor-critic reinforcement learning algorithm capa-ble of learning to select those primitives in a biologically plausible fashion (i.e., through a dynamic competition between postures); c) it proposes a procedure to pre-train the actor to select promising primitives when tackling novel rein-forcement learning tasks. Some tests (obtained with a task used for studying monkeys engaged in learning reaching-action sequences) show that the model is computationally sound and capable of learning to select sensorimotor primi-tives from the postures' continuous space on the basis of their population encoding.