Cascade Models of Synaptically Stored Memories
Open accessAbstract
Storing memories of ongoing, everyday experiences requires a high degree of plasticity, but retaining these memories demands protection against changes induced by further activity and experience. Models in which memories are stored through switch-like transitions in synaptic efficacy are good at storing but bad at retaining memories if these transitions are likely, and they are poor at storage but good at retention if they are unlikely. We construct and study a model in which each synapse has a cascade of states with different levels of plasticity, connected by metaplastic transitions. This cascade model combines high levels of memory storage with long retention times and significantly outperforms alternative models. As a result, we suggest that memory storage requires synapses with multiple states exhibiting dynamics over a wide range of timescales, and we suggest experimental tests of this hypothesis.
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?
If the contribution of CaMKII to synaptic transmission were a net gain that simply adds onto the preexisting excitatory drive of the cell, the system would be unstable and quickly saturate.
“If this were the case, it has been argued that the system would be unstable and quickly saturate (366–371).”
From Synaptic memory and CaMKII (2023) · cites this paper as [369]
In the section on power-law forgetting (page unavailable), the paper says that imbalances between synaptic strengthening and weakening spoil its memory-performance result, and that a repeatedly potentiated synapse can reach a point where it becomes no stronger. It does not connect this to CaMKII adding to a cell’s preexisting excitatory drive, or say that such a gain would make the system unstable and quickly saturate. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe existence of LTD avoids the problem of saturation, which could be essential for a mnemonic device.
“The existence of LTD avoids the problem of saturation, which could be essential for a mnemonic device (366–371).”
From Synaptic memory and CaMKII (2023) · cites this paper as [369]
The paper’s discussion of memory lifetimes says that balancing synaptic potentiation and depression can improve memory performance. But its discussion of saturation describes effects distinct from the mechanism it proposes; our copy does not establish that LTD itself avoids saturation or that this is essential for a mnemonic device. (LLM, conf=0.75)
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
Standing, L. (1973) · Quarterly Journal of Experimental Psychology · doi:10.1080/14640747308400340 · Cited by 968 · see citing works
Wixted, J. T., & Ebbesen, E. B. (1991) · Psychological Science · doi:10.1111/j.1467-9280.1991.tb00175.x · Cited by 513 · cited 2× · see citing works
“…the types 1970; Standing, 1973; Simons, 1996), but forgetting of dynamic biochemical cascades that are ubiquitous does occur and appears to follow power-law rather in biological systems and, in particular, are associated than exponential dynamics (Wixted and Ebbesen, 1991, with synaptic plasticity.”
BackgroundWixted, J. T., & Ebbesen, E. B. (1997) · Memory & Cognition · doi:10.3758/bf03211316 · Cited by 237 · see citing works
Scharf, M. T., Woo, N. H., Lattal, K. M., Young, J. Z., Nguyen, P. V., & Abel, T. (2002) · Journal of Neurophysiology · doi:10.1152/jn.2002.87.6.2770 · Cited by 204 · see citing works
Menzel, R., Manz, G., Menzel, R., & Greggers, U. (2001) · Learning & Memory · doi:10.1101/lm.40001 · Cited by 201 · see citing works
Mauelshagen, J., Sherff, C. M., & Carew, T. J. (1998) · Learning & Memory · doi:10.1101/lm.5.3.246 · Cited by 129 · see citing works
The power law as an emergent property
Anderson, R. B. (2001) · Memory & Cognition · doi:10.3758/bf03195767 · Cited by 93 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Bredt, D.S., and Nicoll, R.A. (2003). AMPA receptor trafficking at and pictures. Journal of Verbal Learning and Verbal Behavior 6, 156-163. excitatory synapses. Neuron 40, 361-379.
- Villarreal, D.M., Do, V., Haddad, E., and Derrick, B.I. (2002). NMDA Hertz, J., Krogh, A., and Palmer, R.G. (1991). Introduction to the receptor antagonists sustain LTP and spatial memory: active pro- Theory of Neural Computation (Redwood City, CA: Addison- cesses mediate LTP decay. Nat. Neurosci. 5, 48-52.
- Wixted, J.T., and Ebbesen, E. (1997). Genuine power curves in for- Manahan-Vaughan, D., and Braunewell, K.H. (1999). Novelty getting. Mem. Cognit. 25, 731-739. acquisition is associated with induction of hippocampal long-term depression. Proc. Natl. Acad. Sci. USA 96, 8739-8744.
- Pavlik, P.I., and Anderson, J.R. (2005). Practice and forgetting ef- ence 300, 1953-1957. fects on vocabulary memory: An activation-based model of the spacing effect. Cogn. Sci, in press.
- Petersen, C.C., Malenka, R.C., Nicoll, R.A., and Hopfield, J.J. (1998). All-or-none potentiation at CA3-CA1 synapses. Proc. Natl. Acad. Sci. USA 95, 4732-4737.
Cited by
Sorted by citations
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
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
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
Interacting adaptive processes with different timescales underlie short-term motor learning
Smith, M. A., Ghazizadeh, A., & Shadmehr, R. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040179 · Cited by 1,113 · see citing works
Compartmentalized dendritic plasticity and input feature storage in neurons
Losonczy, A., Makara, J. K., & Magee, J. C. (2008) · Nature · doi:10.1038/nature06725 · Cited by 598 · see citing works
The criticality hypothesis: how local cortical networks might optimize information processing
Beggs, J. M. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2092 · Cited by 533 · see citing works
Impermanence of dendritic spines in live adult CA1 hippocampus
Attardo, A., Fitzgerald, J. E., & Schnitzer, M. J. (2015) · Nature · doi:10.1038/nature14467 · Cited by 399 · see citing works
The dynamics of memory as a consequence of optimal adaptation to a changing body
Kording, K. P., Tenenbaum, J. B., & Shadmehr, R. (2007) · Nature Neuroscience · doi:10.1038/nn1901 · Cited by 393 · see citing works
The temporal paradox of Hebbian learning and homeostatic plasticity
Zenke, F., Gerstner, W., & Ganguli, S. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.03.015 · Cited by 201 · see citing works
Computational principles of memory
Chaudhuri, R., & Fiete, I. (2016) · Nature Neuroscience · doi:10.1038/nn.4237 · Cited by 213 · see citing works
Tao, C.-L., Liu, Y.-T., Sun, R., Zhang, B., Qi, L., Shivakoti, S., Tian, C.-L., Zhang, P., Lau, P.-M., Zhou, Z. H., et al. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1548-17.2017 · Cited by 207 · see citing works
Fusi, S., Asaad, W. F., Miller, E. K., & Wang, X.-J. (2007) · Neuron · doi:10.1016/j.neuron.2007.03.017 · Cited by 199 · see citing works
Self-citationRigotti, M. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00024 · Cited by 184 · see citing works
The computational nature of memory modification
Gershman, S. J., Monfils, M.-H., Norman, K. A., & Niv, Y. (2017) · eLife · doi:10.7554/elife.23763 · Cited by 172 · see citing works
Theoretical Neuroscience Rising
Abbott, L. F. (2008) · Neuron · doi:10.1016/j.neuron.2008.10.019 · Cited by 163 · see citing works
Self-citationAso, Y., Ray, R. P., Long, X., Bushey, D., Cichewicz, K., Ngo, T.-T., Sharp, B., Christoforou, C., Hu, A., Lemire, A. L., et al. (2019) · eLife · doi:10.7554/elife.49257 · Cited by 141 · see citing works
Nicoll, R. A., & Schulman, H. (2023) · Physiological Reviews · doi:10.1152/physrev.00034.2022 · Cited by 126 · see citing works
Neural mechanism for stochastic behaviour during a competitive game
Soltani, A., Lee, D., & Wang, X.-J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.044 · Cited by 77 · see citing works
Persistent activity in a recurrent circuit underlies courtship memory in Drosophila
Zhao, X., Lenek, D., Dag, U., Dickson, B. J., & Keleman, K. (2018) · eLife · doi:10.7554/elife.31425 · Cited by 74 · see citing works
Binary and analog variation of synapses between cortical pyramidal neurons
Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., Wu, J., Bodor, A. L., Bleckert, A. A., Brittain, D., Kemnitz, N., et al. (2019) · doi:10.1101/2019.12.29.890319 · Cited by 130 · see citing works
“, 2005), and has been the basis of a number of computational models of memory (Tsodyks, 1990; Amit and Fusi, 1994; Fusi et al., 2005).”
BackgroundSynaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation
Tetzlaff, C., Kolodziejski, C., Timme, M., Tsodyks, M., & Wörgötter, F. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003307 · Cited by 54 · see citing works
Long-Term Memory Stabilized by Noise-Induced Rehearsal
Wei, Y., & Koulakov, A. A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3929-12.2014 · Cited by 29 · see citing works
Synaptic memory survives molecular turnover
Lee, J., Chen, X., & Nicoll, R. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2211572119 · Cited by 27 · see citing works
Nugent, M. A., Porter, R., & Kenyon, G. T. (2008) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2008.03.043 · Cited by 2 · see citing works
- Authors
- Fusi, S., Drew, P. J., & Abbott, L. F.
- Affiliations
- University of Bern (CH)
- Brandeis University (US)
- Corresponding author
- Abbott, L.F.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2005-02
- Volume
- 45
- Issue
- 4
- Pages
- 599-611
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2129386380
- Cited by
- 569
- References
- 7
- Citations by year
- 2026: 27 · 2025: 40 · 2024: 31 · 2023: 26 · 2022: 27 · 2021: 37 · 2020: 18 · 2019: 23 · 2018: 17 · 2017: 45 · 2016: 19 · 2015: 31 · 2014: 31 · 2013: 34 · 2012: 19
- FWCI
- 11.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH058754, MH58754)
- National Institute on Drug Abuse (DA016455, R01DA016455)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- synaptic plasticity, memory storage, memory retention, metaplasticity, cascade model, synaptic efficacy, long-term memory, synaptic dynamics
- MeSH terms
- Animals, Humans, Memory, Models, Neurological, Neuronal Plasticity, Synapses, Time Factors
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
