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Repetitive training enhances the pattern recognition capability of cultured neural networks.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Repetitive training enhances the pattern recognition capability of cultured neural networks.
Συγγραφείς: Shao WW; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Shao Q; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Xu HH; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Qiao GJ; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Wang RX; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Ma ZY; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Meng WW; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Yang ZB; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Zang YL; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China., Li XH; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.
Πηγή: PLoS computational biology [PLoS Comput Biol] 2025 Apr 22; Vol. 21 (4), pp. e1013043. Date of Electronic Publication: 2025 Apr 22 (Print Publication: 2025).
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science, [2005]-
Ιατρικοί όροι (MeSH): Nerve Net*/physiology , Pattern Recognition, Physiological*/physiology , Pattern Recognition, Automated*/methods , Neural Networks, Computer*, Neurons/physiology ; Animals ; Computational Biology ; Rats ; Microelectrodes ; Cells, Cultured
Περίληψη: Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.
(Copyright: © 2025 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Competing Interests: The authors have declared that no competing interests exist.
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J Neurosci. 2015 Mar 4;35(9):4040-51. (PMID: 25740531)
Innovation (Camb). 2024 Apr 01;5(3):100620. (PMID: 38706954)
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Brain. 2024 Nov 4;147(11):3817-3833. (PMID: 38739753)
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IEEE Trans Biomed Eng. 2005 Mar;52(3):371-83. (PMID: 15759567)
Neuroscience. 2021 Mar 15;458:108-119. (PMID: 33309966)
Nat Commun. 2022 Jul 29;13(1):4403. (PMID: 35906223)
Sci Rep. 2017 Aug 22;7(1):9080. (PMID: 28831071)
Front Neurosci. 2022 Aug 18;16:854199. (PMID: 36061604)
Sci Rep. 2021 Oct 14;11(1):20407. (PMID: 34650146)
Biosens Bioelectron. 2023 Nov 1;239:115591. (PMID: 37634421)
PLoS One. 2009 Aug 04;4(8):e6482. (PMID: 19652720)
Front Neural Circuits. 2015 Oct 07;9:57. (PMID: 26500505)
Sci Adv. 2020 Sep 25;6(39):. (PMID: 32978164)
Eur J Neurosci. 2008 Jul;28(1):221-37. (PMID: 18662344)
Brain Sci. 2022 Nov 19;12(11):. (PMID: 36421904)
PLoS Comput Biol. 2012;8(5):e1002522. (PMID: 22615555)
Front Comput Neurosci. 2014 Jan 13;7:193. (PMID: 24567714)
Sci Rep. 2022 Jun 10;12(1):9656. (PMID: 35688933)
Neuron. 2012 Feb 9;73(3):415-34. (PMID: 22325196)
PNAS Nexus. 2023 Jun 27;2(6):pgad188. (PMID: 37383023)
J Neural Eng. 2021 May 13;18(4):. (PMID: 33892486)
J Neurosci. 2015 Apr 8;35(14):5459-70. (PMID: 25855164)
Front Neurosci. 2024 Jul 05;18:1387098. (PMID: 39035779)
Learn Mem. 2015 Nov 16;22(12):594-603. (PMID: 26572650)
Front Neural Circuits. 2012 Dec 12;6:99. (PMID: 23248586)
ACS Appl Mater Interfaces. 2022 Apr 6;14(13):15736-15746. (PMID: 35294190)
Sci Rep. 2018 Apr 3;8(1):5578. (PMID: 29615719)
Phys Biol. 2007 Oct 09;4(3):181-93. (PMID: 17928657)
Front Cell Dev Biol. 2022 Jul 01;10:948818. (PMID: 35846370)
Comput Intell Neurosci. 2007;:12725. (PMID: 18350128)
ACS Appl Bio Mater. 2023 Sep 18;6(9):3414-3422. (PMID: 37071831)
IEEE Trans Biomed Eng. 2016 Dec 14;64(11):2555-2561. (PMID: 28113197)
Physiol Rev. 2006 Jul;86(3):1033-48. (PMID: 16816145)
Neuron. 2022 Dec 7;110(23):3952-3969.e8. (PMID: 36228614)
Phys Biol. 2018 Oct 17;15(6):06LT01. (PMID: 30255848)
Q Rev Biophys. 2002 Feb;35(1):63-87. (PMID: 11997981)
Prog Brain Res. 2005;147:189-99. (PMID: 15581706)
Grant Information: National Key Research and Development Program of China; National Natural Science Foundation of China
Entry Date(s): Date Created: 20250422 Date Completed: 20250510 Latest Revision: 20260910
Update Code: 20260910
PubMed Central ID: PMC12064202
DOI: 10.1371/journal.pcbi.1013043
PMID: 40262075
Βάση Δεδομένων: MEDLINE
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  Data: Repetitive training enhances the pattern recognition capability of cultured neural networks.
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  Data: <searchLink fieldCode="AU" term="%22Shao+WW%22">Shao WW</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Shao+Q%22">Shao Q</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Xu+HH%22">Xu HH</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Qiao+GJ%22">Qiao GJ</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Wang+RX%22">Wang RX</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Ma+ZY%22">Ma ZY</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Meng+WW%22">Meng WW</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Yang+ZB%22">Yang ZB</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Zang+YL%22">Zang YL</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.<br /><searchLink fieldCode="AU" term="%22Li+XH%22">Li XH</searchLink>; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.; Haihe Laboratory of Brain - Computer Interaction and Human-Machine Integration, Tianjin, China.; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China.
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  Data: Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.<br /> (Copyright: © 2025 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
– Name: Abstract
  Label: Competing Interests
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  Data: The authors have declared that no competing interests exist.
– Name: Ref
  Label: References
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      – SubjectFull: Neurons physiology
        Type: general
      – SubjectFull: Animals
        Type: general
      – SubjectFull: Computational Biology
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      – SubjectFull: Nerve Net physiology
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      – SubjectFull: Pattern Recognition, Physiological physiology
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      – TitleFull: Repetitive training enhances the pattern recognition capability of cultured neural networks.
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          Name:
            NameFull: Zang YL
      – PersonEntity:
          Name:
            NameFull: Li XH
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 22
              M: 04
              Text: 2025 Apr 22
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 1553-7358
          Numbering:
            – Type: volume
              Value: 21
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: PLoS computational biology
              Type: main
ResultId 1