Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning
There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value‐based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represen...
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| Published in | The European journal of neuroscience Vol. 42; no. 4; pp. 2003 - 2021 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
France
Blackwell Publishing Ltd
01.08.2015
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0953-816X 1460-9568 1460-9568 |
| DOI | 10.1111/ejn.12994 |
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| Abstract | There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value‐based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward‐prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal‐ganglia influence on the dopamine neurons. Here we present a computational neural‐circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up‐regulate and down‐regulate the dopamine neurons via the basal‐ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward‐prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward‐prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward‐reversal learning and punishment‐avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter‐temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed‐circuit mechanistic understanding of appetitive/aversive learning.
There are two popular notions in value‐based learning and choice: (i) dopamine represents reward prediction error, and (ii) the basal‐ganglia direct and indirect pathways are crucial for appetitive and aversive learning, respectively. We present an integrated account for these two, whose relationship has remained unclear. We provide predictions on the activity of cortical and striatal neuron subpopulations and on possible effects of the strengths of the two pathways on subject's time preference. |
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| AbstractList | There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning. There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value‐based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward‐prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal‐ganglia influence on the dopamine neurons. Here we present a computational neural‐circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up‐regulate and down‐regulate the dopamine neurons via the basal‐ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward‐prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward‐prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward‐reversal learning and punishment‐avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter‐temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed‐circuit mechanistic understanding of appetitive/aversive learning. There are two popular notions in value‐based learning and choice: (i) dopamine represents reward prediction error, and (ii) the basal‐ganglia direct and indirect pathways are crucial for appetitive and aversive learning, respectively. We present an integrated account for these two, whose relationship has remained unclear. We provide predictions on the activity of cortical and striatal neuron subpopulations and on possible effects of the strengths of the two pathways on subject's time preference. There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning.There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning. |
| Author | Morita, Kenji Kawaguchi, Yasuo |
| AuthorAffiliation | 2 Division of Cerebral Circuitry National Institute for Physiological Sciences Okazaki Japan 3 Department of Physiological Sciences SOKENDAI (The Graduate University for Advanced Studies) Okazaki Japan 1 Physical and Health Education Graduate School of Education The University of Tokyo 7‐3‐1 Hongo Bunkyo‐ku Tokyo 113‐0033 Japan 4 Japan Science and Technology Agency Core Research for Evolutional Science and Technology Tokyo Japan |
| AuthorAffiliation_xml | – name: 4 Japan Science and Technology Agency Core Research for Evolutional Science and Technology Tokyo Japan – name: 3 Department of Physiological Sciences SOKENDAI (The Graduate University for Advanced Studies) Okazaki Japan – name: 2 Division of Cerebral Circuitry National Institute for Physiological Sciences Okazaki Japan – name: 1 Physical and Health Education Graduate School of Education The University of Tokyo 7‐3‐1 Hongo Bunkyo‐ku Tokyo 113‐0033 Japan |
| Author_xml | – sequence: 1 givenname: Kenji surname: Morita fullname: Morita, Kenji email: : Kenji Morita, as above., morita@p.u-tokyo.ac.jp organization: Physical and Health Education, Graduate School of Education, The University of Tokyo, 7-3-1 Hongo, Tokyo, 113-0033, Bunkyo-ku, Japan – sequence: 2 givenname: Yasuo surname: Kawaguchi fullname: Kawaguchi, Yasuo organization: Division of Cerebral Circuitry, National Institute for Physiological Sciences, Okazaki, Japan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26095906$$D View this record in MEDLINE/PubMed |
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| Snippet | There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value‐based learning: (i) the direct and indirect pathways of... There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of... |
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| SubjectTerms | Animals Appetitive Behavior - physiology Avoidance Learning - physiology Basal Ganglia - cytology Basal Ganglia - physiology Cerebral Cortex - cytology Cerebral Cortex - physiology Choice Behavior Computational Neuroscience Computer Simulation corticostriatal direct pathway dopamine Humans indirect pathway Models, Neurological Nerve Net - physiology Neural Pathways - physiology Neuronal Plasticity - drug effects Neuronal Plasticity - physiology Neurons - physiology Predictive Value of Tests Probability reinforcement learning Reward Time Factors |
| Title | Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning |
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