{"type":"ScholarlyArticle","title":"Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks","authors":[{"name":"Bing Han","slug":"bing-han","given":"Bing","family":"Han","affiliation":"Brain-inspired Cognitive Intelligence Lab , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Feifei Zhao","slug":"feifei-zhao","given":"Feifei","family":"Zhao","affiliation":"Brain-inspired Cognitive Intelligence Lab , Institute of Automation , Chinese Academy of Sciences"},{"name":"Yi Zeng","slug":"yi-zeng","given":"Yi","family":"Zeng","affiliation":"Brain-inspired Cognitive Intelligence Lab , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences; School of Future Technology , University of Chinese Academy of Sciences; Center for Excellence in Brain Science and Intelligence Technology , Chinese Academy of Sciences"},{"name":"Wenxuan Pan","slug":"wenxuan-pan","given":"Wenxuan","family":"Pan","affiliation":"Brain-inspired Cognitive Intelligence Lab , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Guobin Shen","slug":"guobin-shen","given":"Guobin","family":"Shen","affiliation":"Brain-inspired Cognitive Intelligence Lab , Institute of Automation , Chinese Academy of Sciences; School of Future Technology , University of Chinese Academy of Sciences"}],"abstract":"Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing continual learning frameworks are usually applicable to Deep Neural Networks (DNNs) and lack the exploration on more brain-inspired, energy-efficient Spiking Neural Networks (SNNs). Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. When learning a sequence of tasks, the DSD-SNN dynamically assigns and grows new neurons to new tasks and prunes redundant neurons, thereby increasing memory capacity and reducing computational overhead. In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task). We validate the effectiveness of the proposed model on multiple class incremental learning and task incremental learning benchmarks. Extensive experiments demonstrated that our model could significantly improve performance, learning speed and memory capacity, and reduce computational overhead. Besides, our DSD-SNN model achieves comparable performance with the DNNs-based methods, and significantly outperforms the state-of-the-art (SOTA) performance for existing SNNs-based continual learning methods.","doi":"10.24963/ijcai.2023/334","url":"https://ijcai.org/proceedings/2023/334","pdf":"https://ijcai.org/proceedings/2023/0334.pdf","markdown":"https://ijcai.org/proceedings/2023/334.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/334","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Humans and AI","track":"Main Track","number":334,"pages":{"first":2993,"last":3001},"keywords":["Humans and AI: HAI: Cognitive modeling","Machine Learning: ML: Incremental learning"],"videos":[],"references":[{"position":1,"raw":"[Abbott, 1999] Larry F Abbott. 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