Reward learning is a fundamental cognitive function, and the brain has a dedicated neuromodulatory system – based on dopamine – that supports this process. Changes to the dopamine system that are triggered by exposure to drugs of abuse are thought to underlie the behavioral changes observed in addiction. Here we propose to use a treasure trove of previously recorded neural data from throughout the mesocorticostriatal circuitry that supports reward learning, to elucidate the computational role of each component of the circuit, their interactions, and how these components are affected by cocaine. Our brains constantly generate predictions about what rewards might be available, and compare these predictions to actual outcomes. The neuromodulator dopamine is thought to report these ‘prediction error’ signals, the result of the ongoing comparison between expected and obtained rewards, that are key to updating predictions so they are more accurate in the future. Predicting the timing of rewards, and not just their identity or value, is an important component of this process, but it remains a mystery how the brain forms and uses predictions about time in reward learning. Based on a novel theoretical model we recently developed, we will test the computational role of three key brain areas that comprise the brain circuit critical for reward learning, using a state-of-the- art methods from machine learning to jointly decode the learning processes that drive neural activity from multiple brain areas along with behavior as rats perform a reward learning task. In Aim 1, we hypothesize that neural activity in the orbitofrontal cortex is uniquely important for representing high level ‘task states’ and will test for patterns in OFC neural activity that follow the hidden structure of the task. In Aim 2, we will decode the representation of reward predictions about the amount and timing of rewards, and test whether they are separable in VS neural activity. In Aim 3, we will test how activity in VS and OFC controls dopamine activity, and in particular how each input component enables prediction errors to be temporally precise. In Aim 4, we will test how exposure to cocaine changes neural activity that represents reward predictions in the VS, and the impact of this disruption on dopamine prediction errors in the VTA. This innovative multi-level study will leverage numerous existing neural and behavioral data from rats performing a well-validated reward-learning task, to reveal the computational, neural and behavioral mechanisms of the reward prediction and learning circuitry in the brain, and the source of their disruption in addiction.

Dysfunction of mesolimbic and corticostriatal circuitry is implicated in addiction, and is known to produce critical deficits in timing (for example, impulsivity) and reward learning. Here we will use a treasure trove of 8 existing datasets to test novel predictions about the neural basis of timing and reward learning, employing state-of-the-art tools from machine learning to ‘decode’ reward predictions jointly from multiple brain areas and from behavior, in intact neural circuits as well as after damage from lesions or exposure to cocaine. Our findings will lay bare the neural computations that support reward prediction and will allow us to link aberrant reward learning in addiction back to its basis in the circuitry of the brain.