Fetching the paper…
Reading the bibliography…
In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered.
D. O. Hebb et al. , “The organization of behavior,” 1949
1949
Earlier work this paper cites.
J. L. McClelland and D. E. Rumelhart, “An interactive activation model of context effects in letter perception: I. an account of basic findings.” Psychological review , vol. 88, no. 5, p. 375, 1981
1981
Earlier work this paper cites.
M. McCloskey and N. J. Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” The psychology of learning and motivation , vol. 24, no. 109, p. 92, 1989
1989
Earlier work this paper cites.
R. Ratcliff, “Connectionist models of recognition memory: constraints imposed by learning and forgetting functions.” Psychological review , vol. 97, no. 2, p. 285, 1990
1990
Earlier work this paper cites.
P. Földiak, “Forming sparse representations by local anti-hebbian learning,” Biological cybernetics , vol. 64, no. 2, pp. 165–170, 1990
1990
Earlier work this paper cites.
E. Meier, L. Hertz, and A. Schousboe, “Neurotransmitters as developmental signals,” Neurochemistry international , vol. 19, no. 1-2, pp. 1–15, 1991
1991
Earlier work this paper cites.
J. R. Movellan, “Contrastive hebbian learning in the continuous hopfield model,” in Connectionist Models . Elsevier, 1991, pp. 10–17
1991
Earlier work this paper cites.
R. M. French, “Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks,” in Proceedings of the 13th annual cognitive science society conference . Erlbaum, 1991, pp. 173–178
1991
Earlier work this paper cites.
M. J. Swain and D. H. Ballard, “Color indexing,” International journal of computer vision , vol. 7, no. 1, pp. 11–32, 1991
1991
Earlier work this paper cites.
R. M. French, “Semi-distributed representations and catastrophic forgetting in connectionist networks,” Connection Science , vol. 4, no. 3-4, pp. 365–377, 1992
1992
Earlier work this paper cites.
A. Robins, “Catastrophic forgetting in neural networks: the role of rehearsal mechanisms,” in Artificial Neural Networks and Expert Systems, 1993. Proceedings., First New Zealand International Two-Stream Conference on . IEEE, 1993, pp. 65–68
1993
Earlier work this paper cites.
J. L. McClelland, “The grain model: A framework for modeling the dynamics of information processing,” Attention and Performance (Volc. XIV): Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience. , 1993
1993
Earlier work this paper cites.
S. Lewandowsky, “On the relation between catastrophic interference and generalization in connectionist networks,” Journal of Biological Systems , vol. 2, no. 03, pp. 307–333, 1994
1994
Earlier work this paper cites.
S. Thrun and T. M. Mitchell, “Lifelong robot learning,” Robotics and autonomous systems , vol. 15, no. 1-2, pp. 25–46, 1995
1995
Earlier work this paper cites.
——, “Catastrophic forgetting, rehearsal and pseudorehearsal,” Connection Science , vol. 7, no. 2, pp. 123–146, 1995
1995
Earlier work this paper cites.
J. L. McClelland, B. L. McNaughton, and R. C. O’reilly, “Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.” Psychological review , vol. 102, no. 3, p. 419, 1995
1995
Earlier work this paper cites.
——, “Consolidation in neural networks and in the sleeping brain,” Connection Science , vol. 8, no. 2, pp. 259–276, 1996
1996
Earlier work this paper cites.
S. Thrun, “Is learning the n-th thing any easier than learning the first?” in Advances in neural information processing systems , 1996, pp. 640–646
1996
Earlier work this paper cites.
R. C. O’Reilly, “Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm,” Neural computation , vol. 8, no. 5, pp. 895–938, 1996
1996
Earlier work this paper cites.
B. A. Olshausen and D. J. Field, “Sparse coding with an overcomplete basis set: A strategy employed by v1?” Vision research , vol. 37, no. 23, pp. 3311–3325, 1997
1997
Earlier work this paper cites.
R. P. Rao and D. H. Ballard, “Dynamic model of visual recognition predicts neural response properties in the visual cortex,” Neural computation , vol. 9, no. 4, pp. 721–763, 1997
1997
Earlier work this paper cites.
——, “Six principles for biologically based computational models of cortical cognition,” Trends in cognitive sciences , vol. 2, no. 11, pp. 455–462, 1998
1998
Earlier work this paper cites.
R. M. French, “Catastrophic forgetting in connectionist networks,” Trends in cognitive sciences , vol. 3, no. 4, pp. 128–135, 1999
1999
Earlier work this paper cites.
——, “Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.” Nature neuroscience , vol. 2, no. 1, 1999
1999
Earlier work this paper cites.
——, “Generalization in interactive networks: The benefits of inhibitory competition and hebbian learning,” Neural Computation , vol. 13, no. 6, pp. 1199–1241, 2001
2001
Earlier work this paper cites.
M. W. Howard and M. J. Kahana, “A distributed representation of temporal context,” Journal of Mathematical Psychology , vol. 46, no. 3, pp. 269–299, 2002
2002
Earlier work this paper cites.
A. Y. Ng and M. I. Jordan, “On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes,” in Advances in neural information processing systems , 2002, pp. 841–848
2002
Earlier work this paper cites.
E. Yehene, N. Meiran, and N. Soroker, “Basal ganglia play a unique role in task switching within the frontal-subcortical circuits: evidence from patients with focal lesions,” Journal of Cognitive Neuroscience , vol. 20, no. 6, pp. 1079–1093, 2008
2008
Earlier work this paper cites.
G. G. Turrigiano, “The self-tuning neuron: synaptic scaling of excitatory synapses,” Cell , vol. 135, no. 3, pp. 422–435, 2008
2008
Earlier work this paper cites.
K. Ibata, Q. Sun, and G. G. Turrigiano, “Rapid synaptic scaling induced by changes in postsynaptic firing,” Neuron , vol. 57, no. 6, pp. 819–826, 2008
2008
Cited alongside, same era.
K. Friston, “The free-energy principle: a rough guide to the brain?” Trends in cognitive sciences , vol. 13, no. 7, pp. 293–301, 2009
2009
Cited alongside, same era.
H. Adesnik and M. Scanziani, “Lateral competition for cortical space by layer-specific horizontal circuits,” Nature , vol. 464, no. 7292, p. 1155, 2010
2010
Cited alongside, same era.
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 2001–2010
2010
Cited alongside, same era.
2017
Later among the works it cites.
M. Jaderberg, W. M. Czarnecki, S. Osindero, O. Vinyals, A. Graves, D. Silver, and K. Kavukcuoglu, “Decoupled neural interfaces using synthetic gradients,” in International conference on machine learning . PMLR, 2017, pp. 1627–1635
2017
Later among the works it cites.
W. M. Czarnecki, G. Świrszcz, M. Jaderberg, S. Osindero, O. Vinyals, and K. Kavukcuoglu, “Understanding synthetic gradients and decoupled neural interfaces,” in International Conference on Machine Learning . PMLR, 2017, pp. 904–912
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2010
Cited alongside, same era.
A. D. Szlam, K. Gregor, and Y. L. Cun, “Structured sparse coding via lateral inhibition,” in Advances in Neural Information Processing Systems , 2011, pp. 1116–1124
2011
Cited alongside, same era.
Y. Huang and R. P. Rao, “Predictive coding,” Wiley Interdisciplinary Reviews: Cognitive Science , vol. 2, no. 5, pp. 580–593, 2011
2011
Cited alongside, same era.
C. Wacongne, J.-P. Changeux, and S. Dehaene, “A neuronal model of predictive coding accounting for the mismatch negativity,” Journal of Neuroscience , vol. 32, no. 11, pp. 3665–3678, 2012
2012
Cited alongside, same era.
2013
Cited alongside, same era.
S. Grossberg, “Adaptive resonance theory: How a brain learns to consciously attend, learn, and recognize a changing world,” Neural networks , vol. 37, pp. 1–47, 2013
2013
Cited alongside, same era.
G. Leisman, O. Braun-Benjamin, and R. Melillo, “Cognitive-motor interactions of the basal ganglia in development,” Frontiers in systems neuroscience , vol. 8, p. 16, 2014
2014
Cited alongside, same era.
T. J. Buschman and E. K. Miller, “Goal-direction and top-down control,” Philosophical Transactions of the Royal Society B: Biological Sciences , vol. 369, no. 1655, p. 20130471, 2014
2014
Cited alongside, same era.
2017
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Bartunov, A. Santoro, B. Richards, L. Marris, G. E. Hinton, and T. Lillicrap, “Assessing the scalability of biologically-motivated deep learning algorithms and architectures,” in Advances in Neural Information Processing Systems , 2018, pp. 9390–9400
2018
Later among the works it cites.
2018
Later among the works it cites.
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars, “Memory aware synapses: Learning what (not) to forget,” in Proceedings of the European Conference on Computer Vision (ECCV) , September 2018
2018
Later among the works it cites.
2019
Closest in time.
A. G. Ororbia and A. Mali, “Biologically motivated algorithms for propagating local target representations,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 4651–4658
2019
Closest in time.
S. Hou, X. Pan, C. C. Loy, Z. Wang, and D. Lin, “Learning a unified classifier incrementally via rebalancing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 831–839
2019
Closest in time.
2019
Closest in time.
D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne, “Experience replay for continual learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Closest in time.
2019
Closest in time.
A. Ororbia, A. Mali, C. L. Giles, and D. Kifer, “Continual learning of recurrent neural networks by locally aligning distributed representations,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Closest in time.
Y. Liu, Y. Su, A.-A. Liu, B. Schiele, and Q. Sun, “Mnemonics training: Multi-class incremental learning without forgetting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 245–12 254
2020
Closest in time.
G. M. van de Ven, H. T. Siegelmann, and A. S. Tolias, “Brain-inspired replay for continual learning with artificial neural networks,” Nature communications , vol. 11, no. 1, pp. 1–14, 2020
2020
Closest in time.
T. C. Moulin, D. Rayêe, M. J. Williams, and H. B. Schiöth, “The synaptic scaling literature: A systematic review of methodologies and quality of reporting,” Frontiers in cellular neuroscience , vol. 14, p. 164, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
N. Imam and T. A. Cleland, “Rapid online learning and robust recall in a neuromorphic olfactory circuit,” Nature Machine Intelligence , vol. 2, no. 3, pp. 181–191, 2020
2020
Closest in time.
B. Tsuda, K. M. Tye, H. T. Siegelmann, and T. J. Sejnowski, “A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex,” Proceedings of the National Academy of Sciences , vol. 117, no. 47, pp. 29 872–29 882, 2020
2020
Closest in time.
A. Prabhu, P. H. Torr, and P. K. Dokania, “Gdumb: A simple approach that questions our progress in continual learning,” in European conference on computer vision . Springer, 2020, pp. 524–540
2020
Closest in time.
2020
Closest in time.
T. Salvatori, Y. Song, Y. Hong, L. Sha, S. Frieder, Z. Xu, R. Bogacz, and T. Lukasiewicz, “Associative memories via predictive coding,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Closest in time.
A. Ororbia and D. Kifer, “The neural coding framework for learning generative models,” Nature communications , vol. 13, no. 1, pp. 1–14, 2022
2022
Closest in time.
Z. Mai, R. Li, J. Jeong, D. Quispe, H. Kim, and S. Sanner, “Online continual learning in image classification: An empirical survey,” Neurocomputing , vol. 469, pp. 28–51, 2022
2022
Closest in time.