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Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence.
Synaptic density in human frontal cortex-developmental changes and effects of aging
Peter R Huttenlocher et al · 1979
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Naturally-occurring neuron death in the ciliary ganglion of the chick embryo following removal of preganglionic input: evidence for the role of afferents in ganglion cell survival
Susan Furber, Ronald W Oppenheim, and David Prevette · 1987
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Morphometric study of human cerebral cortex development
Peter R Huttenlocher · 1990
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Rethinking innateness: A connectionist perspective on development
Jeffrey L Elman, Elizabeth A Bates, and Mark H Johnson · 1996
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Networks of spiking neurons: The third generation of neural network models
Wolfgang Maass · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Lapicque’s introduction of the integrate-and-fire model neuron (1907)
Larry F Abbott · 1999
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Neural connections: Some you use, some you lose
John T Bruer · 1999
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Development of neural circuitry for precise temporal sequences through spontaneous activity, axon remodeling, and synaptic plasticity
Joseph K Jun and Dezhe Z Jin · 2007
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Molecular and cellular approaches to memory allocation in neural circuits
Alcino J Silva, Yu Zhou, Thomas Rogerson, Justin Shobe, and J Balaji · 2009
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Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor · 2015
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Empirical evaluation of rectified activations in convolutional network
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
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Progressive neural networks
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Continual learning with hypernetworks
Johannes Von Oswald, Christian Henning, Benjamin F Grewe, and João Sacramento · 2020
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Progressive convolutional neural network for incremental learning
Zahid Ali Siddiqui and Unsang Park · 2021
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Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
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Dynamically optimizing network structure based on synaptic pruning in the brain
Feifei Zhao and Yi Zeng · 2021
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Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
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Efficient architecture search for continual learning
Qiang Gao, Zhipeng Luo, Diego Klabjan, and Fengli Zhang · 2022
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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Spatio-temporal backpropagation for training high-performance spiking neural networks
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi · 2018
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Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
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Random path selection for incremental learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Random path selection for continual learning
Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Adaptive sparse structure development with pruning and regeneration for spiking neural networks
Bing Han, Feifei Zhao, Yi Zeng, and Wenxuan Pan · 2022
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Developmental plasticity-inspired adaptive pruning for deep spiking and artificial neural networks
Bing Han, Feifei Zhao, Yi Zeng, and Guobin Shen · 2022
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Foster: Feature boosting and compression for class-incremental learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2022
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Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, et al · 2022
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Toward a brain-inspired developmental neural network based on dendritic spine dynamics
Feifei Zhao, Yi Zeng, and Jun Bai · 2022
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Nature-inspired self-organizing collision avoidance for drone swarm based on reward-modulated spiking neural network
Feifei Zhao, Yi Zeng, Bing Han, Hongjian Fang, and Zhuoya Zhao · 2022
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A framework for the general design and computation of hybrid neural networks
Rong Zhao, Zheyu Yang, Hao Zheng, Yujie Wu, Faqiang Liu, Zhenzhi Wu, Lukai Li, Feng Chen, Seng Song, Jun Zhu, et al · 2022
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Continual prune-and-select: class-incremental learning with specialized subnetworks
Aleksandr Dekhovich, David MJ Tax, Marcel HF Sluiter, and Miguel A Bessa · 2023
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