Probing the neural basis of reward, valuation, and choice

6 October 2017

Read Montague
Computational Psychiatry Unit
Carillon Research Institute
Virginia Tech


Over the last 3 decades computational sciences have invaded our biological understanding of reward, valuation, and choice. One result of this invasion is the mixing of biophysical lexicons that depict ionic channels, cellular processes, and neural function in terms familiar from chemistry and physics with algorithmic descriptions of neural behavior that find their origins in abstract learning or optimization models. In the area of reward processing one particular outcome is that algorithmic ideas now structure the way we describe the dynamical behavior of reward-related neural activity and this now mixes (sometimes profitably) with more traditional biophysical descriptions. I will trace the emergence of computational models of reward from single neurons to whole network responses to show how this invasion has now yielded new methods for sub-second recording and understanding of dopamine delivery in the human nervous system.

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