Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164368
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dc.contributor.authorSuhaimi, Ahmaden_US
dc.contributor.authorLim, Amos W. H.en_US
dc.contributor.authorChia, Xin Weien_US
dc.contributor.authorLi, Chunyueen_US
dc.contributor.authorMakino, Hiroshien_US
dc.date.accessioned2023-01-18T01:27:16Z-
dc.date.available2023-01-18T01:27:16Z-
dc.date.issued2022-
dc.identifier.citationSuhaimi, 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.abn0984en_US
dc.identifier.issn2375-2548en_US
dc.identifier.urihttps://hdl.handle.net/10356/164368-
dc.description.abstractThe 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.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation2018-T1-001-032en_US
dc.relationRT11/19en_US
dc.relationMOE2018-T2-1-021en_US
dc.relationMOE2017-T3-1-002en_US
dc.relation.ispartofScience Advancesen_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.subjectScience::Medicineen_US
dc.titleRepresentation learning in the artificial and biological neural networks underlying sensorimotor integrationen_US
dc.typeJournal Articleen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.identifier.doi10.1126/sciadv.abn0984-
dc.description.versionPublished versionen_US
dc.identifier.pmid35658033-
dc.identifier.scopus2-s2.0-85131702020-
dc.identifier.issue22en_US
dc.identifier.volume8en_US
dc.identifier.spageeabn0984en_US
dc.subject.keywordsBiological Neural Networksen_US
dc.subject.keywordsDecisions Makingsen_US
dc.description.acknowledgementThis 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
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