Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Meta-learning in reinforcement learning
Neural Networks
Representation and timing in theories of the dopamine system
Neural Computation
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Phasic activities of dopamine (DA) neurons in the primate midbrain have been considered as representing temporal difference (TD) errors from a computational perspective. Recently, several studies have reported that, in stochastic reward tasks, the DA activities gradually increase before receiving actual rewards, which is not well explained by the simple TD model. In this study, we propose an alternative model based on a probabilistic formulation of the stochastic reward task. In simulation experiments, expectation errors, defined by the probabilistic modeling, well described the gradually increasing DA activities during a wait period even in a single trial.