کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
404777 | 677451 | 2007 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Multiple model-based reinforcement learning explains dopamine neuronal activity Multiple model-based reinforcement learning explains dopamine neuronal activity](/preview/png/404777.png)
A number of computational models have explained the behavior of dopamine neurons in terms of temporal difference learning. However, earlier models cannot account for recent results of conditioning experiments; specifically, the behavior of dopamine neurons in case of variation of the interval between a cue stimulus and a reward has not been satisfyingly accounted for. We address this problem by using a modular architecture, in which each module consists of a reward predictor and a value estimator. A “responsibility signal”, computed from the accuracy of the predictions of the reward predictors, is used to weight the contributions and learning of the value estimators. This multiple-model architecture gives an accurate account of the behavior of dopamine neurons in two specific experiments: when the reward is delivered earlier than expected, and when the stimulus–reward interval varies uniformly over a fixed range.
Journal: Neural Networks - Volume 20, Issue 6, August 2007, Pages 668–675