کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404508 677431 2008 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
The emergence of saliency and novelty responses from Reinforcement Learning principles
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
The emergence of saliency and novelty responses from Reinforcement Learning principles
چکیده انگلیسی

Recent attempts to map reward-based learning models, like Reinforcement Learning [Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An introduction. Cambridge, MA: MIT Press], to the brain are based on the observation that phasic increases and decreases in the spiking of dopamine-releasing neurons signal differences between predicted and received reward [Gillies, A., & Arbuthnott, G. (2000). Computational models of the basal ganglia. Movement Disorders, 15(5), 762–770; Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1–27]. However, this reward-prediction error is only one of several signals communicated by that phasic activity; another involves an increase in dopaminergic spiking, reflecting the appearance of salient but unpredicted non-reward stimuli [Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15(4–6), 495–506; Horvitz, J. C. (2000). Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience, 96(4), 651–656; Redgrave, P., & Gurney, K. (2006). The short-latency dopamine signal: A role in discovering novel actions? Nature Reviews Neuroscience, 7(12), 967–975], especially when an organism subsequently orients towards the stimulus [Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1–27]. To explain these findings, Kakade and Dayan [Kakade, S., & Dayan, P. (2002). Dopamine: Generalization and bonuses. Neural Networks, 15(4–6), 549–559.] and others have posited that novel, unexpected stimuli are intrinsically rewarding. The simulation reported in this article demonstrates that this assumption is not necessary because the effect it is intended to capture emerges from the reward-prediction learning mechanisms of Reinforcement Learning. Thus, Reinforcement Learning principles can be used to understand not just reward-related activity of the dopaminergic neurons of the basal ganglia, but also some of their apparently non-reward-related activity.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 21, Issue 10, December 2008, Pages 1493–1499
نویسندگان
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