Vijay Namboodiri, PhD

Associate Professor, Department of Neurology

Center for Integrative Neuroscience, University of California, San Francisco

Host(s): Vikram Gadagkar, Gadagkar Lab

Dopamine and causal learning

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 experienced over some fixed time, 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. 

Relevant Publications: 

Mesolimbic dopamine release conveys causal associations

Reward timescale controls the rate of behavioral and dopaminergic learning