Goal-directed feature learning

Only a subset of available sensory information is useful for decision making. Classical models of the brain's sensory system, such as generative models, consider all elements of the sensory stimuli. However, only the action-relevant components of stimuli need to reach the motor control and decision making structures in the brain. To learn these action-relevant stimuli, the part of the sensory system that feeds into a motor control circuit needs some kind of relevance feedback. We propose a simple network model consisting of a feature learning (sensory) layer that feeds into a reinforcement learning (action) layer. Feedback is established by the reinforcement learner's temporal difference (delta) term modulating an otherwise Hebbian-like learning rule of the feature learner. Under this influence, the feature learning network only learns the relevant features of the stimuli, i.e. those features on which goal-directed actions are to be based. With the input preprocessed in this manner, the reinforcement learner performs well in delayed reward tasks. The learning rule approximates an energy function's gradient descent. The model presents a link between reinforcement learning and unsupervised learning and may help to explain how the basal ganglia receive selective cortical input.