Synaptic computation
Abstract
Neurons are often considered to be the computational engines of the brain, with synapses acting solely as conveyers of information. But the diverse types of synaptic plasticity and the range of timescales over which they operate suggest that synapses have a more active role in information processing. Long-term changes in the transmission properties of synapses provide a physiological substrate for learning and memory, whereas short-term changes support a variety of computations. By expressing several forms of synaptic plasticity, a single neuron can convey an array of different signals to the neural circuit in which it operates.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Neuromodulation can regulate single-synapse computations at the hippocampal mossy fiber synapse.
“Our findings indicate that neuromodulation can regulate single-synapse computations at the hippocampal mossy fiber synapse [1,103].”
Cites
Differential signaling via the same axon of neocortical pyramidal neurons
Markram, H., Wang, Y., & Tsodyks, M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.9.5323 · Cited by 1,102 · see citing works
Target-cell-specific facilitation and depression in neocortical circuits
Reyes, A., Lujan, R., Rozov, A., Burnashev, N., Somogyi, P., & Sakmann, B. (1998) · Nature Neuroscience · doi:10.1038/1092 · Cited by 716 · see citing works
Chung, S., Li, X., & Nelson, S. B. (2002) · Neuron · doi:10.1016/s0896-6273(02)00659-1 · Cited by 530 · see citing works
Long-term potentiation and memory
Morris, R. G. M. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1230 · Cited by 232 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Glutamate Receptor Ion Channels: Structure, Regulation, and Function
Traynelis, S. F., Wollmuth, L. P., McBain, C. J., Menniti, F. S., Vance, K. M., Ogden, K. K., Hansen, K. B., Yuan, H., Myers, S. J., & Dingledine, R. (2010) · Pharmacological Reviews · doi:10.1124/pr.109.002451 · Cited by 3,568 · see citing works
Neuroligins and neurexins link synaptic function to cognitive disease
Südhof, T. C. (2008) · Nature · doi:10.1038/nature07456 · Cited by 1,849 · see citing works
Yang, J. C., Mun, J., Kwon, S. Y., Park, S., Bao, Z., & Park, S. (2019) · Advanced Materials · doi:10.1002/adma.201904765 · Cited by 1,641 · see citing works
Internally Generated Cell Assembly Sequences in the Rat Hippocampus
Pastalkova, E., Itskov, V., Amarasingham, A., & Buzsáki, G. (2008) · Science · doi:10.1126/science.1159775 · Cited by 1,301 · see citing works
Neural syntax: cell assemblies, synapsembles, and readers
Buzsáki, G. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.023 · Cited by 1,289 · see citing works
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
Wong, K.-F., & Wang, X.-J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3733-05.2006 · Cited by 1,029 · see citing works
Network abnormalities and interneuron dysfunction in Alzheimer disease
Palop, J. J., & Mucke, L. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.141 · Cited by 1,022 · see citing works
Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679 · see citing works
“Other slow biophysical mechanisms, such as shortterm synaptic facilitation (Abbott and Regehr, 2004) or calciumdependent processes in single cells (Major and Tank, 2004), may also contribute to time integration.”
BackgroundThe quest for a Quantum Neural Network
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0809-8 · Cited by 671 · see citing works
Calcium Channel Regulation and Presynaptic Plasticity
Catterall, W. A., & Few, A. P. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.005 · Cited by 647 · see citing works
Understanding complexity in the human brain
Bassett, D. S., & Gazzaniga, M. S. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.03.006 · Cited by 572 · see citing works
Amyloid-β as a positive endogenous regulator of release probability at hippocampal synapses
Abramov, E., Dolev, I., Fogel, H., Ciccotosto, G. D., Ruff, E., & Slutsky, I. (2009) · Nature Neuroscience · doi:10.1038/nn.2433 · Cited by 499 · see citing works
Short-Term Presynaptic Plasticity
Regehr, W. G. (2012) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a005702 · Cited by 482 · see citing works
Self-citationSilver, R. A. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2864 · Cited by 481 · see citing works
Nonlinear dynamic causal models for fMRI
Stephan, K. E., Kasper, L., Harrison, L. M., Daunizeau, J., den Ouden, H. E. M., Breakspear, M., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.262 · Cited by 416 · see citing works
Fanselow, E. E., Richardson, K. A., & Connors, B. W. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90691.2008 · Cited by 262 · see citing works
Munc13 C2B domain is an activity-dependent Ca2+ regulator of synaptic exocytosis
Shin, O.-H., Lu, J., Rhee, J.-S., Tomchick, D. R., Pang, Z. P., Wojcik, S. M., Camacho-Perez, M., Brose, N., Machius, M., Rizo, J., et al. (2010) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1758 · Cited by 243 · see citing works
Astrocyte lipid metabolism is critical for synapse development and function in vivo
van Deijk, A. F., Camargo, N., Timmerman, J., Heistek, T., Brouwers, J. F., Mogavero, F., Mansvelder, H. D., Smit, A. B., & Verheijen, M. H. G. (2017) · Glia · doi:10.1002/glia.23120 · Cited by 201 · see citing works
Memristive Crossbar Arrays for Storage and Computing Applications
Li, H., Wang, S., Zhang, X., Wang, W., Yang, R., Sun, Z., Feng, W., Lin, P., Wang, Z., Sun, L., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100017 · Cited by 205 · see citing works
Developmental and epileptic encephalopathies: from genetic heterogeneity to phenotypic continuum
Guerrini, R., Conti, V., Mantegazza, M., Balestrini, S., Galanopoulou, A. S., & Benfenati, F. (2023) · Physiological Reviews · doi:10.1152/physrev.00063.2021 · Cited by 196 · see citing works
Ham, S., Kang, M., Jang, S., Jang, J., Choi, S., Kim, T.-W., & Wang, G. (2020) · Science Advances · doi:10.1126/sciadv.aba1178 · Cited by 184 · see citing works
Network structure shapes spontaneous functional connectivity dynamics
Shen, K., Hutchison, R. M., Bezgin, G., Everling, S., & McIntosh, A. R. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4903-14.2015 · Cited by 171 · see citing works
Modelling trial-by-trial changes in the mismatch negativity
Lieder, F., Daunizeau, J., Garrido, M. I., Friston, K. J., & Stephan, K. E. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002911 · Cited by 168 · see citing works
Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
Smith, S. J., Sümbül, U., Graybuck, L. T., Collman, F., Seshamani, S., Gala, R., Gliko, O., Elabbady, L., Miller, J. A., Bakken, T. E., et al. (2019) · eLife · doi:10.7554/elife.47889 · Cited by 156 · see citing works
Wozny, C., & Williams, S. R. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq257 · Cited by 129 · see citing works
Liu, L., Xu, W., Ni, Y., Xu, Z., Cui, B., Liu, J., Wei, H., & Xu, W. (2022) · ACS Nano · doi:10.1021/acsnano.1c08482 · Cited by 138 · see citing works
Rebola, N., Reva, M., Kirizs, T., Szoboszlay, M., Lőrincz, A., Moneron, G., Nusser, Z., & DiGregorio, D. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.014 · Cited by 136 · see citing works
Emerging dynamic memristors for neuromorphic reservoir computing
Cao, J., Zhang, X., Cheng, H., Qiu, J., Liu, X., Wang, M., & Liu, Q. (2022) · Nanoscale · doi:10.1039/d1nr06680c · Cited by 134 · see citing works
Basu, J., Srinivas, K. V., Cheung, S. K., Taniguchi, H., Huang, Z. J., & Siegelbaum, S. A. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.001 · Cited by 122 · see citing works
Vandael, D., Borges-Merjane, C., Zhang, X., & Jonas, P. (2020) · Neuron · doi:10.1016/j.neuron.2020.05.013 · Cited by 114 · see citing works
Oliet, S. H. R., Baimoukhametova, D. V., Piet, R., & Bains, J. S. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2676-06.2007 · Cited by 107 · see citing works
Martins, N. R. B., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F. J., Opris, I., Lebedev, M. A., Swan, M., Garan, S. A., Rosenfeld, J. V., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00112 · Cited by 91 · see citing works
Real-time finger motion recognition using skin-conformable electronics
Cho, H., Lee, I., Jang, J., Kim, J.-H., Lee, H., Park, S., & Wang, G. (2023) · Nature Electronics · doi:10.1038/s41928-023-01012-z · Cited by 87 · see citing works
Antunes, F. M., & Malmierca, M. S. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.721186 · Cited by 71 · see citing works
A New Age of AI: Features and Futures
Cao, L. (2022) · IEEE Intelligent Systems · doi:10.1109/mis.2022.3150944 · Cited by 76 · see citing works
Hernan, A. E., Alexander, A., Jenks, K. R., Barry, J., Lenck-Santini, P.-P., Isaeva, E., Holmes, G. L., & Scott, R. C. (2014) · Neurobiology of Disease · doi:10.1016/j.nbd.2013.11.012 · Cited by 73 · see citing works
Crossmodal interactions in audiovisual emotion processing
Müller, V. I., Cieslik, E. C., Turetsky, B. I., & Eickhoff, S. B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.007 · Cited by 65 · see citing works
Dauvermann, M. R., Whalley, H. C., Romaniuk, L., Valton, V., Owens, D. G. C., Johnstone, E. C., Lawrie, S. M., & Moorhead, T. W. J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.063 · Cited by 49 · see citing works
Altered short-term plasticity in the prefrontal cortex after early life seizures
Hernan, A. E., Holmes, G. L., Isaev, D., Scott, R. C., & Isaeva, E. (2013) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.10.007 · Cited by 49 · see citing works
Energy demand of synaptic transmission at the hippocampal Schaffer-collateral synapse
Liotta, A., Rösner, J., Huchzermeyer, C., Wojtowicz, A., Kann, O., Schmitz, D., Heinemann, U., & Kovács, R. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2012.116 · Cited by 48 · see citing works
Coupling quantum-like cognition with the neuronal networks within generalized probability theory
Khrennikov, A., Ozawa, M., Benninger, F., & Shor, O. (2025) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2025.102923 · Cited by 17 · see citing works
Kim, O., Okamoto, Y., Kaufmann, W. A., Brose, N., Shigemoto, R., & Jonas, P. (2024) · PLOS Biology · doi:10.1371/journal.pbio.3002879 · Cited by 15 · see citing works
- Authors
- Abbott, L. F., & Regehr, W. G.
- Affiliations
- Brandeis University (US)
- Harvard University (US)
- Venue
- Nature
- Published
- 2004-10
- Volume
- 431
- Issue
- 7010
- Pages
- 796-803
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4247763225
- Cited by
- 1,684
- References
- 4
- Citations by year
- 2026: 100 · 2025: 140 · 2024: 148 · 2023: 126 · 2022: 138 · 2021: 134 · 2020: 121 · 2019: 92 · 2018: 106 · 2017: 69 · 2016: 65 · 2015: 58 · 2014: 53 · 2013: 49 · 2012: 45
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- synaptic plasticity, short-term plasticity, long-term synaptic plasticity, learning and memory, neural information processing, synaptic transmission, neural circuits
- MeSH terms
- Animals, Synaptic Transmission, Neuronal Plasticity, Neurons, Sound Localization, Synapses
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