title: TD models of reward predictive responses in dopamine neurons.
abstract: This article focuses on recent modeling studies of dopamine neuron activity and their influence on behavior. Activity of midbrain dopamine neurons is phasically increased by stimuli that increase the animal's reward expectation and is decreased below baseline levels when the reward fails to occur. These characteristics resemble the reward prediction error signal of the temporal difference (TD) model, which is a model of reinforcement learning. Computational modeling studies show that such a dopamine-like reward prediction error can serve as a powerful teaching signal for learning with delayed reinforcement, in particular for learning of motor sequences. Several lines of evidence suggest that dopamine is also involved in 'cognitive' processes that are not addressed by standard TD models. I propose the hypothesis that dopamine neuron activity is crucial for planning processes, also referred to as 'goal-directed behavior', which select actions by evaluating predictions about their motivational outcomes.
authors.affiliation: Computational Neurobiology Laboratory, The Salk Institute, San Diego, CA 92186, USA. rsuri@intopsys.com
authors.forename: Roland E
authors.initials: RE
authors.lastname: Suri
mesh_terms: D000818:Animals; D004298:Dopamine / Q000502:physiology*; D005544:Forecasting / Q000379:methods; D006801:Humans; D008954:Models, Biological; D009474:Neurons / Q000502:physiology*; D012201:Reward
chemical_list: D004298:Dopamine