The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Catastrophic Forgetting, Rehearsal and Pseudorehearsal
Abstract
This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in neural networks, and explores rehearsal mechanisms (the retraining of some of the previously learned information as the new information is added) as a potential solution. We replicate some of the experiments described by Ratcliff (1990), including those relating to a simple 'recency' based rehearsal regime. We then develop further rehearsal regimes which are more effective than recency rehearsal. In particular, 'sweep rehearsal' is very successful at minimizing catastrophic forgetting. One possible limitation of rehearsal in general, however, is that previously learned information may not be available for retraining. We describe a solution to this problem, 'pseudorehearsal', a method which provides the advantages of rehearsal without actually requiring any access to the previously learned information (the original training population) itself. We then suggest an interpretation of these rehearsal mechanisms in the context of a function approximation based account of neural network learning. Both rehearsal and pseudorehearsal may have practical applications, allowing new information to be integrated into an existing network with minimum disruption of old information.
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“Inspired by replay strategies in continual learning (Robins, 1995; Rolnick et al., 2019; De Lange et al., 2021) as well as by the replay phenomena observed in humans (Smolen et al., 2016) and in neural network models (Amiri et al., 2017), we explore whether simple continual learning strategies remain effective for LLMs.”
From Evaluating the Long-Term Memory of Large Language Models (2025) · cites this paper as Robins, 1995
In the citing paper
“While replaying data from pre-training or previous training stages can alleviate forgetting (Lin, 1992; Robins, 1995; Chen et al., 2025), this strategy is data-inefficient and not scalable as we grow the amount of experience.”
From Continual Learning via Sparse Memory Finetuning (2025) · cites this paper as Robins, 1995
In the citing paper
“Before the era of deep learning, Robins [86,87] stated that catastrophic forgetting could be mitigated through rehearsal or pseudo-rehearsal.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [87]
Citations
Cites
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Introduction to the theory of neural computation
Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,410
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
Competitive learning: From interactive activation to adaptive resonance
Grossberg, S. (1987) · Cognitive Science · doi:10.1111/j.1551-6708.1987.tb00862.x · Cited by 1,471
The ART of adaptive pattern recognition by a self-organizing neural network
Carpenter, G. A., & Grossberg, S. (1988) · Computer · doi:10.1109/2.33 · Cited by 1,278
Ratcliff, R. (1990) · Psychological Review · doi:10.1037/0033-295x.97.2.285 · Cited by 939
Artificial Neural Networks: Approximation and Learning Theory
White, H. (1992) · Medical Entomology and Zoology · Cited by 441
Networks of Formal Neurons and Memory Palimpsests
Nadal, J., Toulouse, G., Changeux, J., & Dehaene, S. (1986) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/1/10/008 · Cited by 215
From Statistics to Neural Networks: Theory and Pattern Recognition Applications
Cherkassky, V., Friedman, J. H., & Wechsler, H. L. (1996) · CERN Document Server (European Organization for Nuclear Research) · Cited by 177
Prediction Risk and Architecture Selection for Neural Networks
Moody, J. E. (1994) · Springer eBooks · doi:10.1007/978-3-642-79119-2_7 · Cited by 158
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Learning complex combinations of operations in a hybrid architecture
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Silver, D. (2007)
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de Angulo, V. R., & Carme Torras, A. (1998) · Neural network systems techniques and applications · doi:10.1016/s1874-5946(98)80041-2
Structural drivers of function in information processing networks
Hermundstad, A. M., Brown, K., Bassett, D. S., & Carlson, J. M. (2011) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2011.6190125
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Olson, J. W. (2019) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
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Metadata
- Authors
- ROBINS, A.
- Affiliations
- University of Otago (NZ)
- Corresponding author
- ROBINS, ANTHONY
- ORCID
- ROBINS, ANTHONY
- Venue
- Connection Science
- Publisher
- Informa UK Limited
- Published
- 1995-06
- Volume
- 7
- Issue
- 2
- Pages
- 123-146
- ISSN
- 0954-0091 (ISSN-L)
- 1360-0494
- OpenAlex
- W2116522068
- Cited by
- 833
- References
- 18
- Citations by year
- 2026: 52 · 2025: 79 · 2024: 92 · 2023: 109 · 2022: 94 · 2021: 100 · 2020: 77 · 2019: 68 · 2018: 23 · 2017: 15 · 2016: 4 · 2015: 6 · 2014: 3 · 2013: 8 · 2012: 1
- FWCI
- 6.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Connection Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- catastrophic forgetting, rehearsal, neural network learning, function approximation, incremental learning, knowledge retention
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