Brain-inspired Continuous Learning: Technology, Application and Future

YANG Jing; LI Bin; LI Shaobo; WANG Qi; YU Liya; HU Jianjun; YUAN Kun

doi:10.11999/JEIT210932

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1865-1878

YANG Jing, LI Bin, LI Shaobo, WANG Qi, YU Liya, HU Jianjun, YUAN Kun. Brain-inspired Continuous Learning: Technology, Application and Future[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1865-1878. doi: 10.11999/JEIT210932

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YANG Jing, LI Bin, LI Shaobo, WANG Qi, YU Liya, HU Jianjun, YUAN Kun. Brain-inspired Continuous Learning: Technology, Application and Future[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1865-1878. doi: 10.11999/JEIT210932

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Brain-inspired Continuous Learning: Technology, Application and Future

doi: 10.11999/JEIT210932

YANG Jing^{1, 2},
LI Bin^{2
,
,},
LI Shaobo^{1, 2},
WANG Qi¹,
YU Liya²,
HU Jianjun³,
YUAN Kun²

1.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
2.
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
3.
Department of Computer Science and Engineering, University of South Carolina, Columbia 29208, USA

Funds: The National Key R&D Program of China(2018AAA010804), The National Natural Science Foundation of China (61863005,62162008, 62166005), The Joint Open Fund Project of Key Laboratories of the Ministry of Education ([2020]245)

Received Date: 2021-09-02
Accepted Date: 2021-11-23
Rev Recd Date: 2021-11-19

Available Online: 2021-11-25

Publish Date: 2022-05-25

Abstract

Abstract

Deep learning model facing the non-independent and identically distributed data streams, the old knowledge will be covered by new knowledge, resulting in a significant performance degradation of model. Continuous Learning(CL) can acquire incremental available knowledge from non-independent and identically distributed data streams, continuously accumulate new knowledge without learning from scratch, and achieve human intelligence by imitating brain learning and memory mechanisms. In this paper, the brain-inspired continuous learning methods are reviewed. Firstly, the history of continuous learning is reviewed. Secondly, from the perspective of brain continuous learning mechanism, the research methods of continuous learning are divided into general methods and brain-inspired methods .The current research status of replay, regularization and sparsity, which are commonly used as the methods of continuous learning, are summarized, and their difficulties are analyzed under the existing technical conditions. To this end, four types of brain-inspired methods: synaptic, dual system, sleep and modularization, which are closer to the ability of brain continuous learning, are meticulously analyzed and compared . Finally, the application status of brain-inspire continuous learning are summarized, and the challenges and development of brain-inspire continuous learning under the existing technical conditions are discussed.
- Continuous Learning(CL),
- Brain-inspired,
- Catastrophic forgetting,
- Brain inspired intelligence,
- Sleep-inspired

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References(94)

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