Modelling brain reinforcement learning circuits in health and disease

Animals and humans learn to make nearly optimal decisions based on  received and expected rewards they get from the environment. Recent data  indicate that the prevalent learning signal for such reinforcement learning is the error between the expected (previously learned) and the received reward. In our team we recently developed a minimal biolgically based computational model for how these errors are computed in the brain. The model includes the dynamics of both the dopaminergic nuclei  (the ventral segmental area) and the dynamics of the formation of the persistent-activity  representations of the relevant stimuli in the prefrontal cortex.  In this project, we will extend this model to  understand the role of sensory learning during conditioning tasks. We will also extend our study to understand the role of addictive drug action in the dopaminergic brain system and in the prefrontal cortical  circuits. Addictive drugs considered will be nicotine and alcohol and their interactions will be studied.