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Continual Learning Inspired by Brain Functionality: A Comprehensive Survey.

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Title: Continual Learning Inspired by Brain Functionality: A Comprehensive Survey.
Authors: Aslam, Muhammad Azeem1,2 (AUTHOR) azeem@eurasia.edu, Hamza, Muhammad3 (AUTHOR), Shuangtong, Zhu2 (AUTHOR), Hongfei, Hu1 (AUTHOR), Wei, Xu2 (AUTHOR), Irfan, Muhammad4 (AUTHOR), Jiangbin, Zheng4 (AUTHOR), Aslam, Saba5 (AUTHOR), Khosravi, Mohamadreza (AUTHOR)
Source: International Journal of Intelligent Systems. 7/26/2025, Vol. 2025, p1-30. 30p.
Subjects: Machine learning, Sequential learning, Artificial neural networks, Knowledge transfer, Cognitive ability, Cognitive computing
Abstract: Neural network–based models have shown tremendous achievements in various fields. However, standard AI‐based systems suffer from catastrophic forgetting when undertaking sequential learning of multiple tasks in dynamic environments. Continual learning has emerged as a promising approach to address catastrophic forgetting. It enables AI systems to learn, transfer, augment, fine‐tune, and reuse knowledge for future tasks. The techniques used to achieve continual learning are inspired by the learning processes of the human brain. In this study, we present a comprehensive review of research and recent developments in continual learning, highlighting key contributions and challenges. We discuss essential functions of the biological brain that are pivotal for achieving continual learning and map these functions to the recent machine‐learning methods to aid understanding. Additionally, we offer a critical review of five recent types of continual learning methods inspired by the biological brain. We also provide empirical results, analysis, challenges, and future directions. We hope that this study will benefit both general readers and the research community by offering a complete picture of the latest developments in this field. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell 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.)
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  Data: Continual Learning Inspired by Brain Functionality: A Comprehensive Survey.
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  Data: <searchLink fieldCode="AR" term="%22Aslam%2C+Muhammad+Azeem%22">Aslam, Muhammad Azeem</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> azeem@eurasia.edu</i><br /><searchLink fieldCode="AR" term="%22Hamza%2C+Muhammad%22">Hamza, Muhammad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shuangtong%2C+Zhu%22">Shuangtong, Zhu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hongfei%2C+Hu%22">Hongfei, Hu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Xu%22">Wei, Xu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Irfan%2C+Muhammad%22">Irfan, Muhammad</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiangbin%2C+Zheng%22">Jiangbin, Zheng</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aslam%2C+Saba%22">Aslam, Saba</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khosravi%2C+Mohamadreza%22">Khosravi, Mohamadreza</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 7/26/2025, Vol. 2025, p1-30. 30p.
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  Data: Neural network–based models have shown tremendous achievements in various fields. However, standard AI‐based systems suffer from catastrophic forgetting when undertaking sequential learning of multiple tasks in dynamic environments. Continual learning has emerged as a promising approach to address catastrophic forgetting. It enables AI systems to learn, transfer, augment, fine‐tune, and reuse knowledge for future tasks. The techniques used to achieve continual learning are inspired by the learning processes of the human brain. In this study, we present a comprehensive review of research and recent developments in continual learning, highlighting key contributions and challenges. We discuss essential functions of the biological brain that are pivotal for achieving continual learning and map these functions to the recent machine‐learning methods to aid understanding. Additionally, we offer a critical review of five recent types of continual learning methods inspired by the biological brain. We also provide empirical results, analysis, challenges, and future directions. We hope that this study will benefit both general readers and the research community by offering a complete picture of the latest developments in this field. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell 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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        Value: 10.1155/int/3145236
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        Text: English
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        PageCount: 30
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Sequential learning
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Knowledge transfer
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      – SubjectFull: Cognitive ability
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      – SubjectFull: Cognitive computing
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      – TitleFull: Continual Learning Inspired by Brain Functionality: A Comprehensive Survey.
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              M: 07
              Text: 7/26/2025
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              Y: 2025
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