Literature DB >> 18938058

The emergence of saliency and novelty responses from Reinforcement Learning principles.

Patryk A Laurent1.   

Abstract

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.

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Year:  2008        PMID: 18938058      PMCID: PMC2629355          DOI: 10.1016/j.neunet.2008.09.004

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  16 in total

Review 1.  Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events.

Authors:  J C Horvitz
Journal:  Neuroscience       Date:  2000       Impact factor: 3.590

Review 2.  Is the short-latency dopamine response too short to signal reward error?

Authors:  P Redgrave; T J Prescott; K Gurney
Journal:  Trends Neurosci       Date:  1999-04       Impact factor: 13.837

Review 3.  Metalearning and neuromodulation.

Authors:  Kenji Doya
Journal:  Neural Netw       Date:  2002 Jun-Jul

4.  Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops.

Authors:  Saori C Tanaka; Kenji Doya; Go Okada; Kazutaka Ueda; Yasumasa Okamoto; Shigeto Yamawaki
Journal:  Nat Neurosci       Date:  2004-07-04       Impact factor: 24.884

5.  How visual stimuli activate dopaminergic neurons at short latency.

Authors:  Eleanor Dommett; Véronique Coizet; Charles D Blaha; John Martindale; Véronique Lefebvre; Natalie Walton; John E W Mayhew; Paul G Overton; Peter Redgrave
Journal:  Science       Date:  2005-03-04       Impact factor: 47.728

Review 6.  The short-latency dopamine signal: a role in discovering novel actions?

Authors:  Peter Redgrave; Kevin Gurney
Journal:  Nat Rev Neurosci       Date:  2006-11-08       Impact factor: 34.870

7.  Absolute coding of stimulus novelty in the human substantia nigra/VTA.

Authors:  Nico Bunzeck; Emrah Düzel
Journal:  Neuron       Date:  2006-08-03       Impact factor: 17.173

Review 8.  Dopamine: generalization and bonuses.

Authors:  Sham Kakade; Peter Dayan
Journal:  Neural Netw       Date:  2002 Jun-Jul

9.  Temporal prediction errors in a passive learning task activate human striatum.

Authors:  Samuel M McClure; Gregory S Berns; P Read Montague
Journal:  Neuron       Date:  2003-04-24       Impact factor: 17.173

Review 10.  Predictive reward signal of dopamine neurons.

Authors:  W Schultz
Journal:  J Neurophysiol       Date:  1998-07       Impact factor: 2.714

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7.  Learned value magnifies salience-based attentional capture.

Authors:  Brian A Anderson; Patryk A Laurent; Steven Yantis
Journal:  PLoS One       Date:  2011-11-21       Impact factor: 3.240

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9.  Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice.

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