The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Activity-dependent scaling of quantal amplitude in neocortical neurons
Abstract
Information is stored in neural circuits through long-lasting changes in synaptic strengths. Most studies of information storage have focused on mechanisms such as long-term potentiation and depression (LTP and LTD), in which synaptic strengths change in a synapse-specific manner. In contrast, little attention has been paid to mechanisms that regulate the total synaptic strength of a neuron. Here we describe a new form of synaptic plasticity that increases or decreases the strength of all of a neuron's synaptic inputs as a function of activity. Chronic blockade of cortical culture activity increased the amplitude of miniature excitatory postsynaptic currents (mEPSCs) without changing their kinetics. Conversely, blocking GABA (gamma-aminobutyric acid)-mediated inhibition initially raised firing rates, but over a 48-hour period mESPC amplitudes decreased and firing rates returned to close to control values. These changes were at least partly due to postsynaptic alterations in the response to glutamate, and apparently affected each synapse in proportion to its initial strength. Such 'synaptic scaling' may help to ensure that firing rates do not become saturated during developmental changes in the number and strength of synaptic inputs, as well as stabilizing synaptic strengths during Hebbian modification and facilitating competition between synapses.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“There is compelling evidence from experiments in neuronal cultures that central neurons are able to maintain average firing rates around a homeostatic set point.”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“One such mechanism is synaptic scaling, which allows neurons to detect changes in their own firing rates through a set of calcium-dependent sensors that then regulate receptor trafficking to increase or decrease the accumulation of glutamate receptors at synaptic sites.”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“When cortical or hippocampal neurons are induced to fire more than normal, over many hours firing returns to baseline levels, and similarly, if neuronal firing is reduced over time, neurons compensate and again firing is restored (Fig. 1A) (Turrigiano et al. 1998;Burrone et al. 2002).”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“Synaptic scaling was first identified in cultured neocortical neurons, where it was observed that perturbing network activity generated compensatory changes in synaptic strength that were in the right direction to restore average firing rates to baseline values (Turrigiano et al. 1998).”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“by measuring minis arising from many synapses onto the same neuron, it was observed that modulating network activity induced uniform increases or decreases in the entire mini amplitude distribution, in effect scaling postsynaptic strength up or down (Turrigiano et al. 1998;Desai et al. 2002;Gainey et al. 2009).”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“Interestingly, the same manipulation (activity blockade using TTX or AMPAR antagonists) that only affects mini amplitude in young cultures (Turrigiano et al. 1998;Wierenga et al. 2005) can also induce changes in mini frequency and in presynaptic release probability in older neuronal cultures (Wierenga et al. 2006), through a process that involves postsynaptic hyperpolarization and a drop in”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“Synaptic scaling has now been shown in a variety of central neurons both in vitro and in vivo, including neocortical and hippocampal pyramidal neurons and spinal neurons (O'Brien et al. 1998;Turrigiano et al. 1998;Desai et al. 2002;Stellwagen and Malenka 2006;Goel and Lee 2007;Kim and Tsien 2008;Knogler et al. 2010).”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“Although in neocortical networks the net effects of changes in excitation and inhibition appear to be homeostatic (Rutherford et al. 1998;Turrigiano et al. 1998;Maffei et al. 2004), different classes of inhibitory synapse are regulated differently by lowered activity.”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Turrigiano et al. 1998
In the citing paper
“In response to chronic hyperactivity or hypoactivity, neurons can engage homeostatic scaling-down or scaling-up respectively, in which the strength of excitatory synapses on a neuron is globally modified to maintain neuronal activity in a physiological range (O'Brien et al., 1998, Turrigiano et al., 1998, Turrigiano, 2008).”
From The AMPA Receptor Code of Synaptic Plasticity (2018) · cites this paper as Turrigiano et al., 1998
In the citing paper
“Sample sizes were initially determined based on prior work using social interaction54,65 and intrinsic physiology62, no formal statistical methods were used to pre-determine sample sizes.”
From Insular cortex mediates approach and avoidance responses to social affective stimuli (2018) · cites this paper as [62]
Citations
Cites
Bienenstock, E., Cooper, L., & Munro, P. (1982) · The Journal of Neuroscience · doi:10.1523/jneurosci.02-01-00032.1982 · Cited by 2,896
Liao, D., Hessler, N. A., & Malinow, R. (1995) · Nature · doi:10.1038/375400a0 · Cited by 1,365
Regulatory phosphorylation of AMPA-type glutamate receptors by CaM-KII during long-term potentiation
Barria, A., Muller, D., Derkach, V., Griffith, L. C., & Soderling, T. R. (1997) · Science · doi:10.1126/science.276.5321.2042 · Cited by 1,049
Impulse activity and the patterning of connections during cns development
Shatz, C. J. (1990) · Neuron · doi:10.1016/0896-6273(90)90333-b · Cited by 766
A study of supersensitivity in denervated mammalian skeletal muscle
Axelsson, J., & Thesleff, S. (1959) · The Journal of Physiology · doi:10.1113/jphysiol.1959.sp006233 · Cited by 732
A Physiological Mechanism for Hebb's Postulate of Learning
Stent, G. S. (1973) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.70.4.997 · Cited by 710
Mechanisms Underlying Long-Term Potentiation of Synaptic Transmission
Madison, D. V., Malenka, R. C., & Nicoll, R. A. (1991) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.14.030191.002115 · Cited by 617
Maturation of a Central Glutamatergic Synapse
Wu, G.-Y., Malinow, R., & Cline, H. T. (1996) · Science · doi:10.1126/science.274.5289.972 · Cited by 546
Linden, D. J., & Connor, J. A. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001535 · Cited by 519
The formation and maturation of synapses in the visual cortex of the rat. II. Quantitative analysis
Blue, M. E., & Parnavelas, J. G. (1983) · Journal of Neurocytology · doi:10.1007/bf01181531 · Cited by 506
Research works printed but not linked: 2
- Hebb, D. O. The Organization of Behavior: a Neurophysiological Theory (Wiley, New York, 1949).
- Axelsson, J. & Thesleff, S. Astudy of supersensitivity in denervated mammalian skeletal muscle. J.Physiol. (Lond.) 149, 178–193 (1957).
Cited by
Sorted by citations
Items 1,701–1,800 of 2,247
Lu, H., Diaz-Pier, S., Lenz, M., & Vlachos, A. (2023) · eLife · doi:10.7554/elife.88376.1 · Cited by 4
A deep learning framework for automated and generalized synaptic event analysisPreprint
O’Neill, P. S., Baccino-Calace, M., Rupprecht, P., Friedrich, R. W., Müller, M., & Delvendahl, I. (2024) · eLife · doi:10.7554/elife.98485.1 · Cited by 4
Receptor Dynamics at the Cell Surface Studied Using Functional Tagging
Kittler, J. T., & Moss, S. J. (2006) · CRC Press eBooks · doi:10.1201/9780203486283-12 · Cited by 3
A unified framework to model synaptic dynamics during the sleep–wake cycle
Kinoshita, F. L., Yamada, R. G., Ode, K. L., & Ueda, H. R. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003198 · Cited by 3
Ortiz-Cruz, C. A., Peralta-Ramirez, M., Herrera-Murillo, M. A., Mejia-Ortiz, R. A., Palomero‐Rivero, M., Guo, B., Ramírez-Jarquín, U. N., & López-Huerta, V. G. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.107108 · Cited by 3
Direct evidence for dendritic spine compensation and regeneration in Alzheimer's disease models
Bhembre, N., Boskovic, Z., Willshaw, J. L., Castello-Waldow, T. P., Bonthron, C., Paolino, A., & Opazo, P. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70829 · Cited by 3
Persistent adaptation through dual-timescale regulation of ion channel properties
Mondal, Y., Calabrese, R. L., & Marder, E. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2530340123 · Cited by 3
English, J., McSweeney, D., Geng, J., Howell, E., Ribbe, F., Hinderhofer, M., Proskauer, L., Sebastian, R., Wang, L., Sharf, T., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117115 · Cited by 3
Adaptive and non‐adaptive changes in activity‐deprived presynaptic terminals
Horellou, S., Pascual, O., Triller, A., & Marty, S. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12399 · Cited by 3
Palmer, J., Keane, A., & Gong, P. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005669 · Cited by 3
Regulation of presynaptic homeostatic plasticity by glial signalling in Alzheimer's disease
Cai, Y., & Wang, T. (2024) · The Journal of Physiology · doi:10.1113/jp286751 · Cited by 3
Control of Inhibition-Stabilized Oscillations in Wilson-Cowan Networks with Homeostatic Plasticity
Godin, C., Krause, M. R., Vieira, P. G., Pack, C. C., & Thivierge, J. (2025) · Entropy · doi:10.3390/e27020215 · Cited by 3
Macroautophagy at the service of synapses
Wosnitzka, E., Gambarotto, L., & Nikoletopoulou, V. (2025) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2025.103054 · Cited by 3
Wehle, D. T., Brown, E., Stamenković, V., Kniffen, B., Harsh, F. M., & Smith, S. E. P. (2026) · Journal of Neurochemistry · doi:10.1111/jnc.70353 · Cited by 3
Synaptic Trafficking of AMPA Receptors
Chen, L., & Maghsoodi, B. (2007) · Elsevier eBooks · doi:10.1016/b978-012369437-9/50014-1 · Cited by 3
A New Unsupervised Neural Network for Pattern Recognition with Spiking Neurons
Lorenzo, R. D., Riccardo, R., & Antonio, C. B. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246888 · Cited by 3
Role of Spontaneous Activity in the Maturation of GABAergic Synapses in Embryonic Spinal Circuits
Gonzalez-Islas, C. E., & Wenner, P. (2009) · Springer eBooks · doi:10.1007/978-1-4419-1243-5_3 · Cited by 3
Fernando, S., & Yamada, K. (2012) · Computational Intelligence and Neuroscience · doi:10.1155/2012/968272 · Cited by 3
Long-Term Information Storage by the Interaction of Synaptic and Structural Plasticity
Fauth, M. J., Wörgötter, F., & Tetzlaff, C. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803784-3.00016-0 · Cited by 3
Sousouri, G., & Huber, R. (2019) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-813743-7.00028-1 · Cited by 3
Hebbian crosstalk and input segregation
Rǎdulescu, A., & Adams, P. R. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.08.004 · Cited by 3
Resilience of network activity in preconditioned neurons exposed to ‘stroke-in-a-dish’ insults
Tauskela, J. S., Kuebler, E. S., Thivierge, J., Aylsworth, A., Hewitt, M., Zhao, X., Mielke, J. G., & Martina, M. (2021) · Neurochemistry International · doi:10.1016/j.neuint.2021.105035 · Cited by 3
Consciousness, Exascale Computational Power, Probabilistic Outcomes, and Energetic Efficiency
Stoll, E. A. (2023) · Cognitive Science · doi:10.1111/cogs.13272 · Cited by 3
Friese, M. B., Gujral, T. S., Palanisamy, A., Hemmer, B. M., Culley, D. J., & Crosby, G. J. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1060186 · Cited by 3
Research Progress of Hippocampal Dopamine System Changes in Perioperative Neurocognitive Disorders
Jia, F.-N., Chen, A.-R., Li, H.-H., & Yu, C. (2024) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2302038 · Cited by 3
Koster, K. P., Fyke, Z., Nguyen, T. T. A., Niqula, A., Noriega-González, L. Y., Woolfrey, K. M., Dell’Acqua, M. L., Cologna, S. M., & Yoshii, A. (2024) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2024.1384625 · Cited by 3
Wise, D. L., Lozoya, Y. E., Valakh, V., Isaac, B., Gao, E. Y., Greene, S. B., Bhonsle, A., Lei, Q. L., Cheng, X., Hooser, S. D. V., et al. (2024) · eNeuro · doi:10.1523/eneuro.0366-23.2024 · Cited by 3
Self-citationChronic modulation of cAMP signaling elicits synaptic scaling irrespective of activity
Bagatelas, E. D., & Kavalali, E. T. (2024) · iScience · doi:10.1016/j.isci.2024.110176 · Cited by 3
Wagner, A. R., Sadeghi, S. G., & Merfeld, D. M. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1542496 · Cited by 3
Roy, N., Ithal, D., Mehta, U. M., Basavaraju, R., Bharath, R. D., Bolo, N. R., Thirthalli, J., Gangadhar, B. N., & Keshavan, M. S. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100494 · Cited by 3
Mechanistic explanation of neuroplasticity using equivalent circuits
Nilsson, M. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1716559 · Cited by 3
Béïque, J.-C. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.06887.x · Cited by 3
Illuminating Synapse-Specific Homeostatic Plasticity
Bartlett, T. E., & Wang, Y. T. (2011) · Neuron · doi:10.1016/j.neuron.2011.11.009 · Cited by 3
Re-encoding of associations by recurrent plasticity increases memory capacity
Medina, D., & Leibold, C. (2014) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2014.00013 · Cited by 3
Ramey, S. L., & Ramey, C. T. (1999) · Mental Retardation and Developmental Disabilities Research Reviews · doi:10.1002/(sici)1098-2779(1999)5:1 · Cited by 48
Developmental Roles and Evolutionary Significance of AMPA‐Type Glutamate Receptors
Hirai, S., Hotta, K., & Okado, H. (2018) · BioEssays · doi:10.1002/bies.201800028 · Cited by 3
Can Engineering Principles Help Us Understand Nervous System Robustness?
O’Leary, T. (2018) · History, philosophy and theory of the life sciences · doi:10.1007/978-3-030-01198-7_9 · Cited by 3
Synaptic signalling in a network of dopamine neurons: what prevents proper intercellular crosstalk?
Chen, Y., Kunath, T., Simpson, J., Homer, N. Z. M., & Sylantyev, S. (2020) · FEBS Letters · doi:10.1002/1873-3468.13910 · Cited by 3
Taylor, H. B. C., Tong, R., Jeans, A. F., & Emptage, N. J. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.564081 · Cited by 3
Wu, Y., Yi, X., Gu, X. F., Wang, Q., Jiang, Q., Li, Y., Zhu, M. X., Ding, J., Li, W., & Xu, T. (2023) · iScience · doi:10.1016/j.isci.2023.106322 · Cited by 3
Representation of Sounds in Auditory Cortex of Awake Rats (Dissertation)Preprint
Hromádka, T., & Hromádka, T. (2008) · Nature Precedings · doi:10.1038/npre.2008.2464 · Cited by 3
Diverse homeostatic responses to visual deprivation by uncovering recurrent subnetworksPreprint
Sweeney, Y., Barnes, S. J., & Clopath, C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/312926 · Cited by 3
Shepherd, J. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0244-8 · Cited by 3
The Processing and Storage of Information in Neuronal Memory Systems Across Time Scales
Nachstedt, T. (2018) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-6885 · Cited by 3
Kohen, R., Baldwin, K. T., Garay, P. M., Tsukahara, T., Chen, A., Flynn, C. G. K., Johnson, C. N., Zhao, X., Sutton, M. A., Iwase, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.30.229443 · Cited by 3
Sleep is required for odor exposure to consolidate memory and remodel olfactory synapsesPreprint
Chandra, R., Farah, F., Muñoz-Lobato, F., Bokka, A., Benedetti, K. L., Brueggemann, C., Saifuddin, F. A., Miller, J. M., Li, J., Chang, E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.24.395228 · Cited by 3
Memory destabilization during reconsolidation: a consequence of homeostatic plasticity?Preprint
Amorim, F. E., Chapot, R. L., Moulin, T. C., Lee, J. L. C., & Amaral, O. B. (2021) · Learning & Memory · doi:10.1101/lm.053418.121 · Cited by 3
Lenz, M., Kruse, P., Eichler, A., Straehle, J., Hemeling, H., Stöhr, P., Beck, J., & Vlachos, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.30.560289 · Cited by 3
Using Electrophysiology to Study Homeostatic Plasticity at theDrosophilaNeuromuscular JunctionPreprint
Wang, T., & Frank, C. A. (2024) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top108393 · Cited by 3
Lu, H., Diaz-Pier, S., Lenz, M., & Vlachos, A. (2024) · eLife · doi:10.7554/elife.88376.2 · Cited by 3
Fritz, I., Wang, F., Chirif, R., Malakasis, N., Gjorgjieva, J., & Castro, A. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.26.666968 · Cited by 3
Chopra, R., Zhong, J., Miller, E. S., Tolossa, G. B., Fosque, L. J., Janzen-Meza, J. A., DeKorver, N. W., Guerriero, R. M., Ritter, N. J., Lambo, M. E., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.09.737603 · Cited by 3
Rasopathy-Associated Mutation Ptpn11D61Y has Age-Dependent Effect on Synaptic Vesicle Recycling
Guhathakurta, D., Selzam, F., Petrušková, A., Weiss, E., Akdaş, E. Y., Montenegro‐Venegas, C., Zenker, M., & Fejtová, A. (2024) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-024-01505-1 · Cited by 2
Divergent mechanisms of neural adaptation and instability in the mammalian retina
Khoussine, J., Sawant, A., Gupta, S., Zhai, H., Shahi, P. K., Pattnaik, B. R., Sinha, R., & Hoon, M. (2025) · Current Biology · doi:10.1016/j.cub.2025.06.015 · Cited by 2
Lukin, J., Beckel, M. S., Pedroncini, O., Giusti, S. A., Mauro, G. C. D., Linenberg, I. M., Patop, I. L., Chernomoretz, A., Marín‐Burgin, A., Kadener, S., et al. (2025) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2025.1609772 · Cited by 2
Han, M., Tan, H. L., Mejı́as, R., Chiu, S., Kim, J., Huganir, R. L., & Wang, T. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03415-0 · Cited by 2
Reshaping reservoirs with unsupervised Hebbian adaptation
Cazalets, T., & Dambre, J. (2025) · Nature Communications · doi:10.1038/s41467-025-67137-1 · Cited by 2
Convergence and divergence of molecular mechanisms in Hebbian and homeostatic plasticity
Feighan, K. M., Thakare, H. K., Glasgow, S. D., & Kennedy, T. E. (2026) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2026.1761008 · Cited by 2
Binding as an enabling mechanism for cross-modal plasticity
Park, W. J., & Celikel, T. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106695 · Cited by 2
Modeling binocular competition through Hebbian plasticity and constrained connectivity
Furman, M. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.04.004 · Cited by 2
Developmental Axonal Pruning and Synaptic Plasticity
Scelfo, B., & Buffelli, M. (2009) · Springer eBooks · doi:10.1007/978-0-387-92708-4_5 · Cited by 2
Computational Perspectives on Adult Neurogenesis
Carlson, K. D., Rothganger, F., & Aimone, J. B. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803784-3.00020-2 · Cited by 2
Secreted Reporter Assay Enables Quantitative and Longitudinal Monitoring of Neuronal Activity
Santos, A. C., Chiola, S., Yang, G., Shcheglovitov, A., & Park, S. (2021) · eNeuro · doi:10.1523/eneuro.0518-20.2021 · Cited by 2
Unsupervised learning of perceptual feature combinations
Tamošiūnaitė, M., Tetzlaff, C., & Wörgötter, F. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011926 · Cited by 2
Equal levels of pre- and postsynaptic potentiation produce unequal outcomes
Savtchenko, L. P., & Rusakov, D. A. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0235 · Cited by 2
Xiong, A., Richmond, J. E., & Kim, H. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404969121 · Cited by 2
Lenz, M., Turko, P., Kruse, P., Eichler, A., Chen, Z. A., Rappsilber, J., Vida, I., & Vlachos, A. (2024) · Molecular Brain · doi:10.1186/s13041-024-01153-y · Cited by 3
Homeostatic regulation of a motor circuit through temperature sensing rather than activity sensing
Cannon, D., & Santin, J. M. (2025) · Current Biology · doi:10.1016/j.cub.2025.03.054 · Cited by 2
Recent Advances in the Molecular Mechanisms of Ocular Dominance Plasticity in the Visual Cortex
Qin, Y., Wang, W., Gu, Y., & Shi, X. (2025) · Neuroscience Bulletin · doi:10.1007/s12264-025-01448-7 · Cited by 2
Rennich, B. J., Powers, R. M., Moores, S. P., Hodul, M., & Juo, P. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011786 · Cited by 2
Altered neocortical microcircuitry in the valproic acid rat model of autism
Rinaldi, T. (2006) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3701 · Cited by 2
Homeostatic Plasticity and post-Traumatic Epileptogenesis
Bazhenov, M., Houweling, A. R., Timofeev, I., & B, T. J. S. A. (2008) · Elsevier eBooks · doi:10.1016/b978-012373649-9.50020-x · Cited by 2
Homeostatic Plasticity Hypothesis and Mechanisms of Neocortical Epilepsies
Kapur, J., & Trotter, S. A. (2005) · Epiliepsy currents/Epilepsy currents · doi:10.1111/j.1535-7511.2005.00042.x · Cited by 2
Cooke, R. M., Luco, S., & Parker, D. (2011) · Journal of Comparative Physiology A · doi:10.1007/s00359-011-0683-0 · Cited by 2
Exact ensemble dynamics for spike-timing-dependent plasticity
Adrian, F. A. (2008) · OHSU Digital Commons · doi:10.6083/m4sq8xcb · Cited by 2
Circuit development in somatosensory cortex
Kim, T., Hooks, B. M., & Cheetham, C. E. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814411-4.00007-x · Cited by 2
Dynamic Sense Representation Using Conceptual Fuzzy Sets
Sekiya, H., Kondo, T., Hashimoto, M., & Takagi, T. (2006) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2006.p0859 · Cited by 2
Coupling (reduced) Graphene Oxide to Mammalian Primary Cortical Neurons In Vitro
Monaco, A., Moskalyuk, A., Motylewski, J., Vahidpour, F., Ng, A. M. H., Loh, K. P., Nesládek, M., & Giugliano, M. (2015) · AIMS Materials Science · doi:10.3934/matersci.2015.3.217 · Cited by 2
Simple Modification of Oja Rule Limits L1-Norm of Weight Vector and Leads to Sparse Connectivity
Aparin, V. (2011) · Neural Computation · doi:10.1162/neco_a_00240 · Cited by 2
A Globally Asymptotically Stable Plasticity Rule for Firing Rate Homeostasis
Joshi, P., & Triesch, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87559-8_59 · Cited by 2
Hebbian learning and temporary storage in the convergence-zone model of episodic memory
Howe, M. D., & Miikkulainen, R. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00248-4 · Cited by 2
Synapse and genome: An elusive tête-à-tête
Clark, E., & Nelson, S. B. (2015) · Science Signaling · doi:10.1126/scisignal.aad2441 · Cited by 2
Self-citationSpitzer, M. (2007) · Nervenheilkunde · doi:10.1055/s-0038-1626849 · Cited by 2
Homeostatic Hippocampal Activity against Reduced Glutamatergic Neurotransmission
Ishikawa, T., Okamoto, K., & Ikegaya, Y. (2015) · International Journal of Pharmacology · doi:10.3923/ijp.2015.318.326 · Cited by 2
Target Engagement with Transcranial Current Stimulation
Frӧhlich, F., Alagapan, S., Boyle, M. R., Hamilton, F., Li, G., Lustenberger, C., & Schmidt, S. L. (2016) · Springer eBooks · doi:10.1007/978-3-319-33967-2_11 · Cited by 2
D’Alessandro, L. M., & Harrison, R. V. (2016) · Neural Plasticity · doi:10.1155/2016/3734646 · Cited by 2
Mizusaki, B. E. P., Agnes, E. J., Erichsen, R., & Brunnet, L. G. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.02.035 · Cited by 2
Multi-Layer Neuromorphic Synapse for Reconfigurable Networks
Zjajo, A., Kumar, S. S., & van Leuken, R. (2018) · International Conference on Signal Processing · doi:10.1109/icsp.2018.8652360 · Cited by 2
Hey, look over there: Distraction effects on rapid sequence recall
Miner, D. C., & Tetzlaff, C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0223743 · Cited by 2
Hammond, C., Goaillard, J., Debanne, D., & Gaïarsa, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397032-9.00018-2 · Cited by 2
Development of the locomotor system—Chick embryo
Wenner, P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816477-8.00010-7 · Cited by 2
Kang, J., Eo, J., Lee, D. M., & Park, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117805 · Cited by 2
Chokshi, V. B., Grier, B. D., Dykman, A., Lantz, C. L., Niebur, E., Quinlan, E. M., & Lee, H. (2021) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2021.663282 · Cited by 2
Learning and memory: Scaling new areas
Radulescu, C. I., & Barnes, S. J. (2021) · Current Biology · doi:10.1016/j.cub.2021.04.023 · Cited by 2
Banach, E., Szczepankiewicz, A., Kaczmarek, L., Jaworski, T., & Urban‐Ciećko, J. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.03.024 · Cited by 2
Homing in on homeostatic plasticity
Toonen, R. F. G., & Verhage, M. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.033 · Cited by 2
Homeostatic regulation of intrinsic excitability in hippocampal neurons
O’Leary, T. (2008) · ERA · Cited by 2
Creation of long-term mental representations using the dimension of fractal dendrites
Gagne, R., & Gagné, S. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556229 · Cited by 2
Introduction: tuning the signal: regulation of postsynaptic receptor properties
Pasqualotto, B. A., & Shaw, C. (2000) · Cellular and Molecular Life Sciences · doi:10.1007/pl00000633 · Cited by 2
Neuronal normalization provides effective learning through ineffective synaptic learning rules
Chechik, G., Meilijson, I., & Ruppin, E. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00184-3 · Cited by 2
Metadata
- Authors
- Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B.
- Affiliations
- Brandeis University (US)
- Corresponding author
- Turrigiano, Gina G.
- Venue
- Nature
- Published
- 1998-02
- Volume
- 391
- Issue
- 6670
- Pages
- 892-896
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W1514853588
- Semantic Scholar
- a2bfdaf93cc88c5d56c9b08b910e2e9e1fcfed40
- Cited by
- 2,226
- References
- 30
- Citations by year
- 2026: 53 · 2025: 96 · 2024: 86 · 2023: 76 · 2022: 91 · 2021: 107 · 2020: 110 · 2019: 89 · 2018: 101 · 2017: 97 · 2016: 74 · 2015: 115 · 2014: 91 · 2013: 118 · 2012: 84
- FWCI
- 18.53
- 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 scaling, miniature excitatory postsynaptic currents, neocortical neurons, synaptic plasticity, GABAergic inhibition, glutamate response, firing rate, Hebbian learning, synaptic competition, long-term potentiation and depression, cortical development
- MeSH terms
- Animals, Bicuculline, Cells, Cultured, Membrane Potentials, Synaptic Transmission, Neuronal Plasticity, Tetrodotoxin, Visual Cortex, 2-Amino-5-phosphonovalerate, Receptors, N-Methyl-D-Aspartate, Pyramidal Cells, alpha-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid, Excitatory Amino Acid Antagonists, Glutamic Acid, GABA Antagonists, Rats
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