Brain-inspired replay for continual learning with artificial neural networks
Open accessAbstract
Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these networks are trained on something new, they rapidly forget what was learned before. In the brain, a mechanism thought to be important for protecting memories is the reactivation of neuronal activity patterns representing those memories. In artificial neural networks, such memory replay can be implemented as 'generative replay', which can successfully - and surprisingly efficiently - prevent catastrophic forgetting on toy examples even in a class-incremental learning scenario. However, scaling up generative replay to complicated problems with many tasks or complex inputs is challenging. We propose a new, brain-inspired variant of replay in which internal or hidden representations are replayed that are generated by the network's own, context-modulated feedback connections. Our method achieves state-of-the-art performance on challenging continual learning benchmarks (e.g., class-incremental learning on CIFAR-100) without storing data, and it provides a novel model for replay in the brain.
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In most cases where generative models are used, the generative model exists outside the network itself, which is not biologically realistic for the brain, although there are some exceptions.
“It should be noted that in most these cases the generative model exists outside the network itself, which is not biologically realistic in the case of the brain, although there are some exceptions.150”
From The overfitted brain: Dreams evolved to assist generalization (2021) · cites this paper as [150]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365 · see citing works
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899 · see citing works
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · see citing works
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256 · see citing works
Episodic memory: from mind to brain
Tulving, E. (2002) · Annual Review of Psychology · doi:10.1146/annurev.psych.53.100901.135114 · Cited by 4,950 · see citing works
Li, Z., & Hoiem, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2773081 · Cited by 4,202 · see citing works
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988 · see citing works
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
McCloskey, M., & Cohen, N. J. (1989) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60536-8 · Cited by 3,770 · see citing works
Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsPreprint
Rezende, D. J., Mohamed, S., & Wierstra, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.4082 · Cited by 3,679 · see citing works
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,412 · see citing works
Reactivation of hippocampal ensemble memories during sleep
Wilson, M. A., & McNaughton, B. L. (1994) · Science · doi:10.1126/science.8036517 · Cited by 3,118 · see citing works
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117 · see citing works
Pixel Recurrent Neural NetworksPreprint
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1601.06759 · Cited by 3,111 · see citing works
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460 · see citing works
Catastrophic forgetting in connectionist networks
French, R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01294-2 · Cited by 2,311 · see citing works
Continual Learning Through Synaptic Intelligence
Zenke, F., Poole, B., & Ganguli, S. (2017) · arXiv · doi:10.48550/arxiv.1703.04200 · Cited by 1,784 · see citing works
Metaplasticity: the plasticity of synaptic plasticity
Abraham, W. C., & Bear, M. F. (1996) · Trends in Neurosciences · doi:10.1016/s0166-2236(96)80018-x · Cited by 1,783 · see citing works
Coordinated memory replay in the visual cortex and hippocampus during sleep
Ji, D., & Wilson, M. A. (2007) · Nature Neuroscience · doi:10.1038/nn1825 · Cited by 1,669 · see citing works
Tutorial on Variational Autoencoders
Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · see citing works
DRAW: A Recurrent Neural Network For Image GenerationPreprint
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., & Wierstra, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04623 · Cited by 1,394 · see citing works
Continual Learning with Deep Generative Replay
Shin, H., Lee, J. K., Kim, J., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1705.08690 · Cited by 1,385 · see citing works
Adaptive deconvolutional networks for mid and high level feature learning
Zeiler, M. D., Taylor, G. W., & Fergus, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126474 · Cited by 1,265 · see citing works
Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience
Skaggs, W. E., & McNaughton, B. L. (1996) · Science · doi:10.1126/science.271.5257.1870 · Cited by 1,173 · see citing works
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., & Bengio, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6211 · Cited by 1,089 · see citing works
Mental Imagery: Functional Mechanisms and Clinical Applications
Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.08.003 · Cited by 1,087 · see citing works
Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
Carr, M. F., Jadhav, S. P., & Frank, L. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2732 · Cited by 958 · see citing works
Ratcliff, R. (1990) · Psychological Review · doi:10.1037/0033-295x.97.2.285 · Cited by 892 · see citing works
Catastrophic forgetting, rehearsal and pseudorehearsal
ROBINS, A. (1995) · Connection Science · doi:10.1080/09540099550039318 · Cited by 833 · see citing works
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
Kumaran, D., Hassabis, D., & McClelland, J. L. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.05.004 · Cited by 736 · see citing works
Hippocampal replay is not a simple function of experience
Gupta, A. S., van der Meer, M. A. A., Touretzky, D. S., & Redish, A. D. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.034 · Cited by 733 · see citing works
Three scenarios for continual learningPreprint
van de Ven, G. M., & Tolias, A. S. (2019) · Lirias · doi:10.48550/arxiv.1904.07734 · Cited by 714 · see citing works
Self-citationInternally generated reactivation of single neurons in human hippocampus during free recall
Gelbard-Sagiv, H., Mukamel, R., Harel, M., Malach, R., & Fried, I. (2008) · Science · doi:10.1126/science.1164685 · Cited by 533 · see citing works
Strengthening Individual Memories by Reactivating Them During Sleep
Rudoy, J. D., Voss, J. L., Westerberg, C. E., & Paller, K. A. (2009) · Science · doi:10.1126/science.1179013 · Cited by 532 · see citing works
Conway, M. A. (2009) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2009.02.003 · Cited by 519 · see citing works
On Tiny Episodic Memories in Continual LearningPreprint
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., & Ranzato, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10486 · Cited by 506 · see citing works
Human replay spontaneously reorganizes experience
Liu, Y., Dolan, R. J., Kurth-Nelson, Z., & Behrens, T. E. J. (2019) · Cell · doi:10.1016/j.cell.2019.06.012 · Cited by 497 · see citing works
Progress & Compress: A scalable framework for continual learningPreprint
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska‐Barwińska, A., Teh, Y. W., Pascanu, R., & Hadsell, R. T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06370 · Cited by 422 · see citing works
A cortical-hippocampal-cortical loop of information processing during memory consolidation
Rothschild, G., Eban, E., & Frank, L. M. (2017) · Nature Neuroscience · doi:10.1038/nn.4457 · Cited by 387 · see citing works
Foster, D. J. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031538 · Cited by 375 · see citing works
A Note on the Inception ScorePreprint
Barratt, S. T., & Sharma, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01973 · Cited by 366 · see citing works
Biasing the content of hippocampal replay during sleep
Bendor, D., & Wilson, M. A. (2012) · Nature Neuroscience · doi:10.1038/nn.3203 · Cited by 359 · see citing works
Upgrading the sleeping brain with targeted memory reactivation
Oudiette, D., & Paller, K. A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.01.006 · Cited by 350 · see citing works
Parallel processing by cortical inhibition enables context-dependent behavior
Kuchibhotla, K. V., Gill, J. V., Lindsay, G. W., Papadoyannis, E. S., Field, R. E., Sten, T. H., Miller, K. D., & Froemke, R. C. (2016) · Nature Neuroscience · doi:10.1038/nn.4436 · Cited by 350 · see citing works
van de Ven, G. M., Trouche, S., McNamara, C. G., Allen, K., & Dupret, D. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.020 · Cited by 320 · see citing works
Self-citationHippocampal place cells construct reward related sequences through unexplored space
Ólafsdóttir, H. F., Barry, C., Saleem, A. B., Hassabis, D., & Spiers, H. J. (2015) · eLife · doi:10.7554/elife.06063 · Cited by 319 · see citing works
Memory reprocessing in corticocortical and hippocampocortical neuronal ensembles
Qin, Y., McNaughton, B. L., Skaggs, W. E., & Barnes, C. A. (1997) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1997.0139 · Cited by 308 · see citing works
Sparse but not ‘Grandmother-cell’ coding in the medial temporal lobe
Quiroga, R. Q., Kreiman, G., Christof Koch, A., & Fried, I. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.12.003 · Cited by 291 · see citing works
The neurobiology of human category learning
Ashby, F. G., & Ell, S. W. (2001) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01624-7 · Cited by 285 · see citing works
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Masse, N. Y., Grant, G. D., & Freedman, D. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803839115 · Cited by 260 · see citing works
Online Continual Learning with Maximal Interfered RetrievalPreprint
Aljundi, R., Belilovsky, E., Tuytelaars, T., Charlin, L., Caccia, M., Lin, M., & Caccia, L. (2019) · arXiv (Cornell University) · Cited by 245 · see citing works
Maintaining memories by reactivation
Rasch, B., & Born, J. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.11.007 · Cited by 240 · see citing works
ALICE: Towards Understanding Adversarial Learning for Joint Distribution MatchingPreprint
Li, C.-Y., Liu, H., Chen, C.-Y., Pu, Y., Chen, L.-Q., Henao, R., & Carin, L. (2017) · Neural Information Processing Systems · doi:10.48550/arxiv.1709.01215 · Cited by 230 · see citing works
Lack of long-term cortical reorganization after macaque retinal lesions
Smirnakis, S. M., Brewer, A. A., Schmid, M. C., Tolias, A. S., Schüz, A., Augath, M. A., INHOFFEN, W., Wandell, B. A., & Logothetis, N. K. (2005) · Nature · doi:10.1038/nature03495 · Cited by 220 · see citing works
Self-citationInternally generated sequences in learning and executing goal-directed behavior
Pezzulo, G., van der Meer, M. A. A., Lansink, C. S., & Pennartz, C. M. A. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.06.011 · Cited by 217 · see citing works
Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines
Hsu, Y.-C., Liu, Y.-C., Ramasamy, A., & Kira, Z. (2018) · arXiv · doi:10.48550/arxiv.1810.12488 · Cited by 211 · see citing works
Experience-Dependent Plasticity in Adult Visual Cortex
Karmarkar, U. R., & Dan, Y. (2006) · Neuron · doi:10.1016/j.neuron.2006.11.001 · Cited by 208 · see citing works
Towards Robust Evaluations of Continual LearningPreprint
Farquhar, S., & Gal, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09733 · Cited by 202 · see citing works
Generative replay with feedback connections as a general strategy for continual learningPreprint
van de Ven, G. M., & Tolias, A. S. (2018) · Lirias · doi:10.48550/arxiv.1809.10635 · Cited by 194 · see citing works
Self-citationContinual Unsupervised Representation LearningPreprint
Rao, D., Visin, F., Rusu, A., Teh, Y. W., Pascanu, R., & Hadsell, R. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.14481 · Cited by 191 · see citing works
Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2018.00078 · Cited by 126 · see citing works
Experience Replay for Continual LearningPreprint
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., & Wayne, G. (2019) · arXiv (Cornell University) · Cited by 124 · see citing works
Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2018) · Institutional Repositories DataBase (IRDB) · Cited by 120 · see citing works
Integration of Cognitive and Motivational Context Information in the Primate Prefrontal Cortex
Watanabe, M., & Sakagami, M. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm067 · Cited by 107 · see citing works
Generative Predictive Codes by Multiplexed Hippocampal Neuronal Tuplets
Liu, K., Sibille, J., & Dragoi, G. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.047 · Cited by 63 · see citing works
Cellular and Systems Consolidation of Declarative Memory
Genzel, L., & Wixted, J. T. (2017) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-319-45066-7_1 · Cited by 24 · see citing works
Generative Models from the perspective of Continual Learning
Lesort, T., Caselles-Dupré, H., Garcia-Ortiz, M., Stoian, A., & Filliat, D. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851986 · Cited by 13 · see citing works
Lack of long-term cortical reorganization after macaque retinal lesions
Smirnakis, S., Brewer, A. A., Schmid, M., et al. (2005) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2005.08.008 · Cited by 10 · see citing works
Simple Lifelong Learning MachinesPreprint
Dey, J. K., Vogelstein, J. T., Helm, H. S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12908 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [36]Goodfellow, I. et al. Generative adversarial nets. In Advances in Neural Information Processing Systems (eds. Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) 2672–2680 (Curran Associates, Inc., Montreal, 2014).
- [38]Rezende, D. J. & Mohamed, S. Variational inference with normalizing flows. In International Conference on Machine Learning (eds. Bach, F. R. and Blei, D. M.) 1530–1538 (JMLR.org, Lille, 2015).
- [53]Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O. & Gelly, S. Assessing generative models via precision and recall. In Advances in Neural Information Processing Systems (eds. Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R.) 5228–5237 (Curran Associates, Inc., Montreal, 2018).
Cited by
Sorted by citations
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Wang, L., Zhang, X., Su, H., & Zhu, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367329 · Cited by 1,113 · see citing works
“BI-R [412] incorporates context-modulated feedback connections in a standard VAE to replay internal”
BackgroundMethodInfluentialThree types of incremental learning
van de Ven, G. M., Tuytelaars, T., & Tolias, A. S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00568-3 · Cited by 665 · see citing works
Self-citationReplay in Deep Learning: Current Approaches and Missing Biological Elements
Hayes, T. L., Krishnan, G. P., Bazhenov, M., Siegelmann, H. T., Sejnowski, T. J., & Kanan, C. (2021) · Neural Computation · doi:10.1162/neco_a_01433 · Cited by 106 · see citing works
Self-citationApical drive-A cellular mechanism of dreaming?
Aru, J., Siclari, F., Phillips, W. A., & Storm, J. F. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.09.018 · Cited by 74 · see citing works
The overfitted brain: Dreams evolved to assist generalization
Hoel, E. (2021) · Patterns · doi:10.1016/j.patter.2021.100244 · Cited by 70 · see citing works
Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
Aleixo, E. L., Colonna, J. G., Cristo, M., & Fernandes, E. (2023) · arXiv · doi:10.48550/arxiv.2312.10549 · Cited by 54
Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
Aleixo, E. L., Colonna, J. G., Cristo, M., & Fernandes, E. (2024) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2024.3966 · Cited by 54 · see citing works
Learning cortical representations through perturbed and adversarial dreaming
Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022) · eLife · doi:10.7554/elife.76384 · Cited by 28 · see citing works
- Authors
- van de Ven, G. M., Siegelmann, H. T., & Tolias, A. S.
- Affiliations
- University of Massachusetts Amherst (US)
- Baylor College of Medicine (US)
- Venue
- Nature Communications
- Published
- 2020-08-13
- Volume
- 11
- Issue
- 1
- Pages
- 4069-4069
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7426273
- OpenAlex
- W3048941751
- Cited by
- 474
- References
- 79
- Citations by year
- 2026: 53 · 2025: 96 · 2024: 98 · 2023: 102 · 2022: 74 · 2021: 46 · 2020: 5
- FWCI
- 22.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (cc-by)
- Apollo
- Funders
- International Brain Research Organization
- United States Department of Defense | Defense Advanced Research Projects Agency (HR0011-18-2-0025)
- ODNI | Intelligence Advanced Research Projects Activity (D16PC00003)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- continual learning, catastrophic forgetting, memory replay, generative replay, brain-inspired learning, class-incremental learning, feedback connections, memory reactivation
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