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DC Field | Value | Language |
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dc.contributor.author | Suhaimi, Ahmad | en_US |
dc.contributor.author | Lim, Amos W. H. | en_US |
dc.contributor.author | Chia, Xin Wei | en_US |
dc.contributor.author | Li, Chunyue | en_US |
dc.contributor.author | Makino, Hiroshi | en_US |
dc.date.accessioned | 2023-01-18T01:27:16Z | - |
dc.date.available | 2023-01-18T01:27:16Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Suhaimi, A., Lim, A. W. H., Chia, X. W., Li, C. & Makino, H. (2022). Representation learning in the artificial and biological neural networks underlying sensorimotor integration. Science Advances, 8(22), eabn0984-. https://dx.doi.org/10.1126/sciadv.abn0984 | en_US |
dc.identifier.issn | 2375-2548 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/164368 | - |
dc.description.abstract | The integration of deep learning and theories of reinforcement learning (RL) is a promising avenue to explore novel hypotheses on reward-based learning and decision-making in humans and other animals. Here, we trained deep RL agents and mice in the same sensorimotor task with high-dimensional state and action space and studied representation learning in their respective neural networks. Evaluation of thousands of neural network models with extensive hyperparameter search revealed that learning-dependent enrichment of state-value and policy representations of the task-performance-optimized deep RL agent closely resembled neural activity of the posterior parietal cortex (PPC). These representations were critical for the task performance in both systems. PPC neurons also exhibited representations of the internally defined subgoal, a feature of deep RL algorithms postulated to improve sample efficiency. Such striking resemblance between the artificial and biological networks and their functional convergence in sensorimotor integration offers new opportunities to better understand respective intelligent systems. | en_US |
dc.description.sponsorship | Ministry of Education (MOE) | en_US |
dc.description.sponsorship | Nanyang Technological University | en_US |
dc.language.iso | en | en_US |
dc.relation | 2018-T1-001-032 | en_US |
dc.relation | RT11/19 | en_US |
dc.relation | MOE2018-T2-1-021 | en_US |
dc.relation | MOE2017-T3-1-002 | en_US |
dc.relation.ispartof | Science Advances | en_US |
dc.rights | © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). | en_US |
dc.subject | Science::Medicine | en_US |
dc.title | Representation learning in the artificial and biological neural networks underlying sensorimotor integration | en_US |
dc.type | Journal Article | en |
dc.contributor.school | Lee Kong Chian School of Medicine (LKCMedicine) | en_US |
dc.identifier.doi | 10.1126/sciadv.abn0984 | - |
dc.description.version | Published version | en_US |
dc.identifier.pmid | 35658033 | - |
dc.identifier.scopus | 2-s2.0-85131702020 | - |
dc.identifier.issue | 22 | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.spage | eabn0984 | en_US |
dc.subject.keywords | Biological Neural Networks | en_US |
dc.subject.keywords | Decisions Makings | en_US |
dc.description.acknowledgement | This work was supported by the NARSAD Young Investigator Grant, the Brain & Behavior Research Foundation (to H.M.); Nanyang Assistant Professorship, Nanyang Technological University (to H.M.); Singapore Ministry of Education Academic Research Fund Tier 1 2018-T1-001-032 (to H.M.); Singapore Ministry of Education Academic Research Fund Tier 1 RT11/19 (to H.M.); Singapore Ministry of Education Academic Research Fund Tier 2 MOE2018-T2-1-021 (to H.M.); and Singapore Ministry of Education Academic Research Fund Tier 3 MOE2017-T3-1-002 (to H.M.) | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | LKCMedicine Journal Articles |
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File | Description | Size | Format | |
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sciadv.abn0984.pdf | 6.81 MB | Adobe PDF | View/Open |
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