A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice.
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| Title: | A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice. |
|---|---|
| Authors: | Bathellier, Brice1,2 brice.bathellier@unic.cnrs-gif.fr, Sui Poh Tee1, Hrovat, Christina1, Rumpel, Simon1 |
| Source: | Proceedings of the National Academy of Sciences of the United States of America. 12/3/2013, Vol. 110 Issue 49, p19950-19955. 6p. |
| Subjects: | Learning, Reinforcement learning, Stochastic learning models, Laboratory mice, Neurons |
| Abstract: | Both in humans and in animals, different individuals may learn the same task with strikingly different speeds; however, the sources of this variability remain elusive. In standard learning models, interindividual variability is often explained by variations of the learning rate, a parameter indicating how much synapses are updated on each learning event. Here, we theoretically show that the initial connectivity between the neurons involved in learning a task is also a strong determinant of how quickly the task is learned, provided that connections are updated in a multiplicative manner. To experimentally test this idea, we trained mice to perform an auditory Go/NoGo discrimination task followed by a reversal to compare learning speed when starting from naive or already trained synaptic connections. All mice learned the initial task, but often displayed sigmoid-like learning curves, with a variable delay period followed by a steep increase in performance, as often observed in operant conditioning. For all mice, learning was much faster in the subsequent reversal training. An accurate fit of all learning curves could be obtained with a reinforcement learning model endowed with a multiplicative learning rule, but not with an additive rule. Surprisingly, the multiplicative model could explain a large fraction of the interindividual variability by variations in the initial synaptic weights. Altogether, these results demonstrate the power of multiplicative learning rules to account for the full dynamics of biological learning and suggest an important role of initial wiring in the brain for predispositions to different tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 92775014 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bathellier%2C+Brice%22">Bathellier, Brice</searchLink><relatesTo>1,2</relatesTo><i> brice.bathellier@unic.cnrs-gif.fr</i><br /><searchLink fieldCode="AR" term="%22Sui+Poh+Tee%22">Sui Poh Tee</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hrovat%2C+Christina%22">Hrovat, Christina</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rumpel%2C+Simon%22">Rumpel, Simon</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink>. 12/3/2013, Vol. 110 Issue 49, p19950-19955. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+learning+models%22">Stochastic learning models</searchLink><br /><searchLink fieldCode="DE" term="%22Laboratory+mice%22">Laboratory mice</searchLink><br /><searchLink fieldCode="DE" term="%22Neurons%22">Neurons</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Both in humans and in animals, different individuals may learn the same task with strikingly different speeds; however, the sources of this variability remain elusive. In standard learning models, interindividual variability is often explained by variations of the learning rate, a parameter indicating how much synapses are updated on each learning event. Here, we theoretically show that the initial connectivity between the neurons involved in learning a task is also a strong determinant of how quickly the task is learned, provided that connections are updated in a multiplicative manner. To experimentally test this idea, we trained mice to perform an auditory Go/NoGo discrimination task followed by a reversal to compare learning speed when starting from naive or already trained synaptic connections. All mice learned the initial task, but often displayed sigmoid-like learning curves, with a variable delay period followed by a steep increase in performance, as often observed in operant conditioning. For all mice, learning was much faster in the subsequent reversal training. An accurate fit of all learning curves could be obtained with a reinforcement learning model endowed with a multiplicative learning rule, but not with an additive rule. Surprisingly, the multiplicative model could explain a large fraction of the interindividual variability by variations in the initial synaptic weights. Altogether, these results demonstrate the power of multiplicative learning rules to account for the full dynamics of biological learning and suggest an important role of initial wiring in the brain for predispositions to different tasks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1073/pnas.1312125110 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 19950 Subjects: – SubjectFull: Learning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Stochastic learning models Type: general – SubjectFull: Laboratory mice Type: general – SubjectFull: Neurons Type: general Titles: – TitleFull: A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bathellier, Brice – PersonEntity: Name: NameFull: Sui Poh Tee – PersonEntity: Name: NameFull: Hrovat, Christina – PersonEntity: Name: NameFull: Rumpel, Simon IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 12 Text: 12/3/2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 00278424 Numbering: – Type: volume Value: 110 – Type: issue Value: 49 Titles: – TitleFull: Proceedings of the National Academy of Sciences of the United States of America Type: main |
| ResultId | 1 |
