title: Learning of sequential movements by neural network model with dopamine-like reinforcement signal.
abstract: Dopamine neurons appear to code an error in the prediction of reward. They are activated by unpredicted rewards, are not influenced by predicted rewards, and are depressed when a predicted reward is omitted. After conditioning, they respond to reward-predicting stimuli in a similar manner. With these characteristics, the dopamine response strongly resembles the predictive reinforcement teaching signal of neural network models implementing the temporal difference learning algorithm. This study explored a neural network model that used a reward-prediction error signal strongly resembling dopamine responses for learning movement sequences. A different stimulus was presented in each step of the sequence and required a different movement reaction, and reward occurred at the end of the correctly performed sequence. The dopamine-like predictive reinforcement signal efficiently allowed the model to learn long sequences. By contrast, learning with an unconditional reinforcement signal required synaptic eligibility traces of longer and biologically less-plausible durations for obtaining satisfactory performance. Thus, dopamine-like neuronal signals constitute excellent teaching signals for learning sequential behavior.
authors.affiliation: Institute of Physiology, University of Fribourg, Switzerland.
authors.forename: R E
authors.initials: RE
authors.lastname: Suri
authors.forename: W
authors.initials: W
authors.lastname: Schultz
mesh_terms: D000465:Algorithms; D000818:Animals; D001479:Basal Ganglia / Q000737:chemistry / Q000502:physiology; D004298:Dopamine / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D015398:Signal Transduction / Q000502:physiology; D013997:Time Factors
chemical_list: D004298:Dopamine