A framework for mesencephalic dopamine systems based on predictive Hebbian learning.
We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.
Montague
P R
PR
Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.
Dayan
P
P
Sejnowski
T J
TJ
D000818:Animals; D002755:Choice Behavior / Q000502:physiology; D003657:Decision Making / Q000502:physiology; D004298:Dopamine / Q000502:physiology*; D005544:Forecasting; D006801:Humans; D007858:Learning / Q000502:physiology*; D008636:Mesencephalon / Q000502:physiology*; D008959:Models, Neurological
D004298:Dopamine