Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma
Open accessAbstract
The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Current AI systems employ binary retention strategies that either preserve everything within their capacity or lose it entirely.
“current AI systems employ binary retention strategies that either preserve everything within their capacity or lose it entirely [16, 17].”
From FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory (2026) · cites this paper as [17]
Section 3, “Current Challenges in AI: Catastrophic Forgetting” (page unavailable), says traditional neural networks can rapidly overwrite previously acquired knowledge when learning new tasks. It does not describe retention as binary or say AI systems preserve everything within capacity or lose it entirely; the same section describes methods that selectively protect or reinforce prior knowledge. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · 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
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · 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
Bliss, T. V. P., & Gardner-Medwin, A. R. (1973) · The Journal of Physiology · doi:10.1113/jphysiol.1973.sp010274 · Cited by 7,460 · 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
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
Malenka, R. C., & Bear, M. F. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.012 · Cited by 3,968 · 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
Diekelmann, S., & Born, J. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2762 · Cited by 3,760 · 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
Rasch, B., & Born, J. (2013) · Physiological Reviews · doi:10.1152/physrev.00032.2012 · Cited by 2,783 · see citing works
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Tononi, G., & Cirelli, C. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.025 · Cited by 2,356 · 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
Place Cells, Grid Cells, and the Brain's Spatial Representation System
Moser, E. I., Kropff, E., & Moser, M.-B. (2008) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.31.061307.090723 · Cited by 1,934 · see citing works
Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning
Buzsáki, G. (2015) · Hippocampus · doi:10.1002/hipo.22488 · Cited by 1,759 · see citing works
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works
Human emotion and memory: Interactions of the amygdala and hippocampal complex
Phelps, E. A. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.03.015 · Cited by 1,577 · see citing works
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
Pattern separation in the hippocampus
Yassa, M. A., & Stark, C. E. L. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.06.006 · Cited by 1,544 · see citing works
Mastering Atari, Go, chess and shogi by planning with a learned model
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2020) · RePEc: Research Papers in Economics · Cited by 1,310 · see citing works
Mastering Atari, Go, chess and shogi by planning with a learned model
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2020) · Nature · doi:10.1038/s41586-020-03051-4 · Cited by 1,214 · see citing works
The Molecular and Systems Biology of Memory
Kandel, E. R., Dudai, Y., & Mayford, M. R. (2014) · Cell · doi:10.1016/j.cell.2014.03.001 · Cited by 1,134 · see citing works
NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD).
Luscher, C., & Malenka, R. C. (2012) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a005710 · Cited by 1,110 · see citing works
AMPARs and Synaptic Plasticity: The Last 25 Years
Huganir, R. L., & Nicoll, R. A. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.025 · Cited by 968 · see citing works
A Critical Period for Enhanced Synaptic Plasticity in Newly Generated Neurons of the Adult Brain
Ge, S., Yang, C.-H., Hsu, K.-S., Ming, G.-L., & Song, H. (2007) · Neuron · doi:10.1016/j.neuron.2007.05.002 · Cited by 927 · see citing works
Appraising the brain's energy budget
Raichle, M. E., & Gusnard, D. A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.172399499 · Cited by 888 · see citing works
Predictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836 · 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
Reinforcement learning, fast and slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.006 · Cited by 667 · see citing works
Rhythms of the hippocampal network
Colgin, L. L. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.21 · Cited by 654 · see citing works
Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective
Botvinick, M. M., Niv, Y., & Barto, A. G. (2009) · Cognition · doi:10.1016/j.cognition.2008.08.011 · Cited by 646 · see citing works
Regulation and function of adult neurogenesis: from genes to cognition.
Aimone, J. B., Li, Y., Lee, S. W., Clemenson, G. D., Deng, W., & Gage, F. H. (2014) · Physiological Reviews · doi:10.1152/physrev.00004.2014 · Cited by 635 · see citing works
The mechanisms for pattern completion and pattern separation in the hippocampus
Rolls, E. T. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2013.00074 · Cited by 545 · see citing works
Experience Replay for Continual Learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., & Wayne, G. (2018) · arXiv · doi:10.48550/arxiv.1811.11682 · Cited by 529 · see citing works
Understanding brain networks and brain organization
Pessoa, L. (2014) · Physics of Life Reviews · doi:10.1016/j.plrev.2014.03.005 · Cited by 464 · see citing works
Mermillod, M., Bugaïska, A., & Bonin, P. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00504 · Cited by 425 · see citing works
Theories of Error Back-Propagation in the Brain
Whittington, J. C. R., & Bogacz, R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.005 · Cited by 420 · see citing works
The Role of Hippocampal Replay in Memory and Planning
Ólafsdóttir, H. F., Bush, D., & Barry, C. (2018) · Current Biology · doi:10.1016/j.cub.2017.10.073 · Cited by 413 · 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
Kumaran, D., & McClelland, J. L. (2012) · Psychological Review · doi:10.1037/a0028681 · Cited by 358 · see citing works
O'Reilly, R. C., & Norman, K. A. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)02005-3 · Cited by 351 · see citing works
From Knowing to Remembering: The Semantic–Episodic Distinction
Renoult, L., Irish, M., Moscovitch, M., & Rugg, M. D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.09.008 · Cited by 346 · see citing works
Hippocampal ripples and memory consolidation
Girardeau, G., & Zugaro, M. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.02.005 · Cited by 330 · see citing works
The Cambridge Handbook of Artificial Intelligence
Alonso, E. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139046855 · Cited by 280 · see citing works
Complementary learning systems
O’Reilly, R. C., Bhattacharyya, R., Howard, M. D., & Ketz, N. (2014) · Cognitive Science · doi:10.1111/j.1551-6709.2011.01214.x · Cited by 259 · see citing works
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
Dai, X., Yin, H., & Jha, N. K. (2019) · IEEE Transactions on Computers · doi:10.1109/tc.2019.2914438 · Cited by 233 · see citing works
Re-evaluating Circuit Mechanisms Underlying Pattern Separation
Cayco-Gajic, N. A., & Silver, R. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.044 · Cited by 220 · see citing works
The successor representation: Its computational logic and neural substrates
Gershman, S. J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0151-18.2018 · Cited by 191 · see citing works
Dendritic solutions to the credit assignment problem
Richards, B. A., & Lillicrap, T. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.08.003 · Cited by 148 · see citing works
Bliss, T. V. P., & Lømo, T. (1973) · The Journal of Physiology · doi:10.1113/jphysiol.1973.sp010273 · Cited by 135 · see citing works
The form and function of hippocampal context representations
Smith, D. M., & Bulkin, D. A. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.01.005 · Cited by 122 · see citing works
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks
Güçlü, U., & van Gerven, M. A. J. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00007 · Cited by 113 · see citing works
Hippocampal Place Fields Maintain a Coherent and Flexible Map across Long Timescales
Kinsky, N. R., Sullivan, D. W., Mau, W., Hasselmo, M. E., & Eichenbaum, H. B. (2018) · Current Biology · doi:10.1016/j.cub.2018.09.037 · Cited by 105 · see citing works
Theta Coordinated Error-Driven Learning in the Hippocampus
Ketz, N., Morkonda, S. G., & O’Reilly, R. C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003067 · Cited by 69 · see citing works
Memory-based Parameter AdaptationPreprint
Sprechmann, P., Jayakumar, S. M., Rae, J. W., Pritzel, A., Badia, A. P., Uría, B., Vinyals, O., Hassabis, D., Pascanu, R., & Blundell, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10542 · Cited by 54 · see citing works
A hippocampal circuit mechanism to balance memory reactivation during sleep
Karaba, L. A., Robinson, H. L., Harvey, R. E., Chen, W., Fernández‐Ruiz, A., & Oliva, A. (2024) · Science · doi:10.1126/science.ado5708 · Cited by 43 · see citing works
Artificial intelligence insights into hippocampal processing
Wirtshafter, H. S., & Wilson, M. A. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.1044659 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [17]Schaul T. Quan J. Antonoglou I. Silver D. Prioritized experience replay arXiv 2015 1511.05952
- [52]Han S. Pool J. Tran J. Dally W. Learning both weights and connections for efficient neural network Adv. Neural Inf. Process. Syst. 2015 28 1135 1143
- [56]Marcus G. Deep learning: A critical appraisal arXiv 2018 1801.00631
- [64]Brown T.B. Mann B. Ryder N. Subbiah M. Kaplan J. Dhariwal P. Neelakantan A. Shyam P. Sastry G. Askell A. Language models are few-shot learners arXiv 2020 2005.14165
Cited by
Sorted by citations
Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113 · see citing works
Selective Forgetting in Machine Learning and Beyond: A Survey
Sha, A. S., Nunes, B. P., & Armin Haller, A. (2026) · ACM Computing Surveys · doi:10.1145/3796542 · Cited by 2 · see citing works
FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory
Wei, L., Dong, X., Peng, X., Xie, N., & Wang, B. (2026) · ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp55912.2026.11462095 · Cited by 1 · see citing works
Brain-inspired artificial intelligence for self-healing microgrids: a comprehensive review
Cruz, J. D. L., Rosero, D., Gómez-Luna, E., Candelo-Becerra, J. E., Kumar, P., Panwar, V. S., Anand, R., & Guerrero, J. M. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11635-1 · Cited by 1 · see citing works
FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory
Wei, L., Peng, X., Dong, X., Xie, N., & Wang, B. (2026) · arXiv
“…balances retention and forgetting through natural decay processes, where unim-portant information gradually fades while significant memories are reinforced, current AI systems employ binary retention strategies that either preserve everything within their capacity or lose it entirely [16, 17].”
Ghallab, H., Nasr, M. M., & Fahmy, H. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160414
Rudroff, T. (2025) · Neurocultural health and wellbeing · doi:10.1007/978-3-032-11596-6_3
Self-citationEpilogue: Sedentary Minds in a Digital Age: The Hidden Costs of Modern Comfort
Biesalski, H. K. (2025) · Springer eBooks · doi:10.1007/978-3-031-99121-9_16
Bridging the Gap Between Theoretical and Practical Reinforcement Learning in Undergraduate Education
Atif, M. A., & Shaikh, M. S. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328225
Khajgiwale, P. A., & Agrawal, S. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330802
Rundo, F., Spata, M. O., Pino, C., Calabretta, M., Messina, A. A., Rundo, M., & Battiato, S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132490
Hasan, R., & Mahmood, S. (2026) · Journal of Informatics and Web Engineering · doi:10.33093/jiwe.2026.5.2.20
Cognition, lifestyle, and health in the age of AI: A review
Alsobaie, S. F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44253-7.00001-1
- Authors
- Rudroff, T., Rainio, O., & Klén, R.
- Affiliations
- University of Turku (FI)
- Corresponding author
- Rudroff, Thorsten
- Venue
- Brain Sciences
- Publisher
- MDPI AG
- Published
- 2024-10-31
- Volume
- 14
- Issue
- 11
- Pages
- 1111
- ISSN
- 2076-3425 (ISSN-L)
- PMC
- PMC11591613
- OpenAlex
- W4403950449
- Cited by
- 11
- References
- 61
- Citations by year
- 2026: 5 · 2025: 6
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UTUPub (University of Turku) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- stability-plasticity dilemma, hippocampus, complementary learning systems, memory consolidation, sharp-wave ripples, offline consolidation, neuroplasticity, continuous learning, long-term memory retention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
