Description:

VIJAY MOHAN K NAMBOODIRI, Ph.D.

Scott Alan Myers Endowed Assistant Professor, Dept of Neurology

Until recently, it was believed that the algorithm and neural circuit for associative learning was understood. The algorithm is temporal difference (TD) reinforcement learning, with the critical teaching signal of a reward prediction error (RPE) conveyed by mesolimbic dopamine. This consensus has recently been challenged by many publications, including ours. My lab developed an alternative theory for associative learning and dopamine function based on the retrospective identification of causes of meaningful outcomes such as rewards. This algorithm makes several surprising and counterintuitive predictions, some of which directly oppose those of the RPE model. My talk will present the results of attempts to test and falsify these predictions. Specifically, I will show evidence that 1) acquisition of behavioral and dopaminergic responses during cue-reward learning is independent of the number of trials so long as the total time is kept constant, 2) the original memory maintained after extinction is that of the retrospective cue-outcome association (with novel implications for addiction and PTSD treatment), 3) mesolimbic dopamine does not abide by TD-RPE in a strong test, and 4) mesolimbic dopamine ramps reflect environmental timescales.