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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.
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“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 1,101–1,200 of 2,247
Modularity Induced Gating and Delays in Neuronal Networks
Idelson, M. S., Cohen, G., Ben‐Jacob, E., & Hanein, Y. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004883 · Cited by 26
The Synaptic Scaling Literature: A Systematic Review of Methodologies and Quality of Reporting
Moulin, T. C., Rayêe, D., Williams, M. J., & Schiöth, H. B. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.00164 · Cited by 26
Bhaumik, B., & Mathur, M. (2003) · Journal of Computational Neuroscience · doi:10.1023/a:1021911019241 · Cited by 26
Lim, R., Callister, R. J., & Brichta, A. M. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91223.2008 · Cited by 26
Homeostatic Regulation of Glutamate Release in Response to Depolarization
Moulder, K. L., Meeks, J. P., & Mennerick, S. (2006) · Molecular Neurobiology · doi:10.1385/mn:33:2:133 · Cited by 26
Tuning of synaptic responses: an organizing principle for optimization of neural circuits
O’Donnell, C., & Nolan, M. F. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2010.10.003 · Cited by 26
Ohtsuki, G., Kawaguchi, S., Mishina, M., & Hirano, T. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.2240-04.2004 · Cited by 26
Gibson, J. R., Bartley, A. F., & Huber, K. M. (2006) · Journal of Neurophysiology · doi:10.1152/jn.01203.2005 · Cited by 26
Cortés, R. Y., Arévalo, J. C., Magby, J. P., Chao, M. V., & Plummer, M. R. (2007) · Developmental Neurobiology · doi:10.1002/dneu.20542 · Cited by 26
Persistent synaptic scaling independent of AMPA receptor subunit composition
Altimimi, H. F., & Stellwagen, D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1102-13.2013 · Cited by 25
Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness
Xu, M., Liu, F., Hu, Y., Li, H., Wei, Y., Zhong, S., Pei, J., & Deng, L. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3373599 · Cited by 25
Venkatesan, S., Subramaniam, S., Rajeev, P., Chopra, Y., Jose, M., & Nair, D. K. (2020) · eNeuro · doi:10.1523/eneuro.0407-19.2020 · Cited by 25
Population temporal structure supplements the rate code during sensorimotor transformations
Jagadisan, U. K., & Gandhi, N. J. (2022) · Current Biology · doi:10.1016/j.cub.2022.01.015 · Cited by 25
Ciganok-Hückels, N., Jehasse, K., Kricsfalussy-Hrabár, L., Ritter, M., Rüland, T., & Kampa, B. M. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac467 · Cited by 25
Homeostatic Synaptic Plasticity
Turrigiano, G. G. (2008) · Springer eBooks · doi:10.1007/978-0-387-77232-5_18 · Cited by 25
Self-citationCopi, A., Jüngling, K., & Gottmann, K. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00155.2005 · Cited by 25
A Spiking Network Model of Decision Making Employing Rewarded STDP
Skorheim, S. W., Lonjers, P., & Bazhenov, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090821 · Cited by 25
Remodeling the Plasticity Debate: The Presynaptic Locus Revisited
Krueger, S., & Fitzsimonds, R. M. (2006) · Physiology · doi:10.1152/physiol.00013.2006 · Cited by 25
Gröschel, M., Ryll, J., Götze, R., Ernst, A., & Basta, D. (2014) · BioMed Research International · doi:10.1155/2014/909260 · Cited by 25
Determinants of electrical properties in developing neurons
Baines, R., & Pym, E. C. G. (2005) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2005.11.003 · Cited by 25
Homeostatic Activity-Dependent Tuning of Recurrent Networks for Robust Propagation of Activity
Gjorgjieva, J., Evers, J. F., & Eglen, S. J. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2511-15.2016 · Cited by 25
Cannon, J. J., & Miller, P. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00253.2016 · Cited by 25
Cooperative stochastic binding and unbinding explain synaptic size dynamics and statistics
Shomar, A., Geyrhofer, L., Ziv, N. E., & Brenner, N. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005668 · Cited by 25
Tran, T., Gallagher, M., & Kirkwood, A. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.06.008 · Cited by 25
Non-scaling regulation of AMPA receptors in homeostatic synaptic plasticity
Wang, G., Zhong, J., Guttieres, D., & Man, H. (2019) · Neuropharmacology · doi:10.1016/j.neuropharm.2019.107700 · Cited by 25
Sang, I. W. F., Schroer, J., Halbhuber, L., Warm, D., Yang, J., Luhmann, H. J., Kilb, W., & Sinning, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22126575 · Cited by 26
Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods
Venkadesh, S., & Horn, J. D. V. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.752332 · Cited by 25
Age-dependent Homeostatic Plasticity of GABAergic Signaling in Developing Retinal Networks
Hennig, M. H., Grady, J. P., van Coppenhagen, J., & Sernagor, E. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.3112-11.2011 · Cited by 25
Nagano, T. (2003) · Molecular and Cellular Neuroscience · doi:10.1016/s1044-7431(03)00172-6 · Cited by 25
Synaptic Scaling and the Development of a Motor Network
Knogler, L. D., Liao, M., & Drapeau, P. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0880-10.2010 · Cited by 24
Experience-dependent plasticity of visual cortical microcircuits
Medini, P. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.08.022 · Cited by 24
Lezmy, J., Gelman, H., Katsenelson, M., Styr, B., Tikochinsky, E., Lipinsky, M., Peretz, A. S., Slutsky, I., & Attali, B. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1914-19.2020 · Cited by 24
Bhandari, A., Ward, T. W., SMITH, J., & Hook, M. J. V. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2022.01.029 · Cited by 24
Retinoic acid-gated BDNF synthesis in neuronal dendrites drives presynaptic homeostatic plasticity
Thapliyal, S., Arendt, K. L., Lau, A. G., & Chen, L. (2022) · eLife · doi:10.7554/elife.79863 · Cited by 25
Valakh, V., Wise, D. L., Zhu, X. A., Sha, M., Fok, J., Hooser, S. D. V., Schectman, R., Cepeda, I., Kirk, R., O'Toole, S. M., et al. (2023) · eLife · doi:10.7554/elife.74899 · Cited by 24
Self-citationNetwork-timing-dependent plasticity
Delattre, V., Keller, D. X., Perich, M. G., Markram, H., & Muller, E. B. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00220 · Cited by 24
Relative contributions of CA3 and medial entorhinal cortex to memory in rats
O’Reilly, K. C., Alarcón, J. M., & Ferbinteanu, J. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00292 · Cited by 24
Emergence of Coordinated Plasticity in the Cochlear Nucleus and Cerebellum
Yang, H., & Xu‐Friedman, M. A. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0167-12.2012 · Cited by 24
Becker, D., Ikenberg, B. D., Schiener, S., Maggio, N., & Vlachos, A. (2014) · Neuropharmacology · doi:10.1016/j.neuropharm.2014.07.013 · Cited by 24
Daur, N., Bryan, A. S., Garcia, V. J., & Bucher, D. M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2584-12.2012 · Cited by 24
Normal protein composition of synapses in Ts65Dn mice: a mouse model of Down syndrome
Fernandez, F., Trinidad, J. C., Blank, M., Feng, D., Burlingame, A. L., & Garner, C. C. (2009) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2009.06110.x · Cited by 24
Petrus, E., Anguh, T. T., Pho, H., Angela, L., Gammon, N., & Lee, H. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00111.2011 · Cited by 24
He, Q., Wang, P., & Tian, N. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07484.x · Cited by 24
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Shah, R. D., & Crair, M. C. (2008) · The Journal of Physiology · doi:10.1113/jphysiol.2008.157222 · Cited by 24
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Kennedy, A. J., & Sweatt, J. D. (2016) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409238.2016.1150958 · Cited by 24
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Beyeler, A., Eckhardt, C. A., & Tye, K. M. (2014) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-420170-5.00012-x · Cited by 24
McGregor, N. W., Thompson, N., O’Connell, K. S., Emsley, R. A., van der Merwe, L., & Warnich, L. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.01.044 · Cited by 24
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Ménard, C., Gaudreau, P., & Quirion, R. (2015) · Handbook of experimental pharmacology · doi:10.1007/978-3-319-16522-6_3 · Cited by 24
Rodrı́guez, G., Mesik, L., Gao, M., Parkins, S., Saha, R., & Lee, H. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2117-18.2019 · Cited by 24
Conde, K., & Roepke, T. A. (2019) · Neuroendocrinology · doi:10.1159/000503146 · Cited by 24
Murase, S., Winkowski, D. E., Liu, J., Kanold, P. O., & Quinlan, E. M. (2019) · eLife · doi:10.7554/elife.52503 · Cited by 24
Remodeling of the Homer-Shank interactome mediates homeostatic plasticity
Heavner, W. E., Lautz, J. D., Speed, H. E., Gniffke, E. P., Immendorf, K. B., Welsh, J. P., Baertsch, N. A., & Smith, S. E. P. (2021) · Science Signaling · doi:10.1126/scisignal.abd7325 · Cited by 24
Acquisition and Performance of Delayed-response Tasks: a Neural Network Model
Gisiger, T., Kerszberg, M., & Changeux, J. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh149 · Cited by 24
One cell to rule them all, and in the dendrites bind them
Costa, R. P. (2011) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2011.00005 · Cited by 24
Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current
Shin, J., Koch, C., & Douglas, R. J. (1999) · Neural Computation · doi:10.1162/089976699300016016 · Cited by 24
McGregor, J. N., Farris, C. A., Ensley, S., Schneider, A. M., Fosque, L. J., Wang, C., Tilden, E. I., Liu, Y., Tu, J., Elmore, H., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.08.006 · Cited by 23
Song, Y., Zhang, J., & Chen, C. (2015) · Scientific Reports · doi:10.1038/srep16257 · Cited by 23
Emulating homeoplasticity phenomena with organic electrochemical devices
Koutsouras, D. A., Malliaras, G. G., & Gkoupidenis, P. (2018) · MRS Communications · doi:10.1557/mrc.2018.53 · Cited by 23
Khoshkhou, M., & Montakhab, A. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00073 · Cited by 23
Sequence learning, prediction, and replay in networks of spiking neurons
Bouhadjar, Y., Wouters, D. J., Diesmann, M., & Tetzlaff, T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010233 · Cited by 23
Distinct molecular pathways govern presynaptic homeostatic plasticity
Nair, A. G., Muttathukunnel, P., & Müller, M. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110105 · Cited by 23
dos Santos, F. P., Vohryzek, J., & Verschure, P. F. M. J. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011279 · Cited by 23
Gonzalez-Islas, C. E., García-Bereguiain, M. Á., & Wenner, P. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1229-12.2012 · Cited by 23
Corti, V., Sánchez-Ruíz, Y., Piccoli, G., Bergamaschi, A., Cannistraci, C. V., Pattini, L., Cerutti, S., Bachi, A., Alessio, M., & Malgaroli, A. (2008) · BMC Neuroscience · doi:10.1186/1471-2202-9-36 · Cited by 23
Astrocytic TLR4 at the crossroads of inflammation and seizure susceptibility
Henneberger, C., & Steinhäuser, C. (2016) · The Journal of Cell Biology · doi:10.1083/jcb.201611078 · Cited by 23
A postsynaptic PI3K-cII dependent signaling controller for presynaptic homeostatic plasticity
Hauswirth, A. G., Ford, K., Wang, T., Fetter, R. D., Tong, A., & Davis, G. W. (2018) · eLife · doi:10.7554/elife.31535 · Cited by 23
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Peláez, J. R., & Piqueira, J. R. C. (2007) · Springer eBooks · doi:10.1007/0-387-37452-3_6 · Cited by 23
Neural oligarchy: how synaptic plasticity breeds neurons with extreme influencePreprint
Kleberg, F. I., & Triesch, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/361394 · Cited by 23
Impact of distinct neurotransmitter release modes on neuronal signaling
Zhang, K. J., Monteggia, L. M., & Kavalali, E. T. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03373-7 · Cited by 22
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Optogenetic stimulation reveals a latent tipping point in cortical networks during ictogenesis
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Jeon, I., & Kim, T. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2023.1092185 · Cited by 24
Ketamine induced synaptic plasticity operates independently of long-term potentiation
Piazza, M. K., Kavalali, E. T., & Monteggia, L. M. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01895-2 · Cited by 22
Deletion of Dlx1 results in reduced glutamatergic input to hippocampal interneurons
Jones, D. L., Howard, M. A., Stanco, A., Rubenstein, J. L. R., & Baraban, S. C. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00056.2011 · Cited by 22
Homeostatic Presynaptic Suppression of Neuronal Network Bursts
Cohen, D., & Segal, M. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91085.2008 · Cited by 22
Wen, J. A., DeBlois, M. C., & Barth, A. L. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3575-12.2013 · Cited by 22
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Burbank, K. S., & Kreiman, G. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002393 · Cited by 22
Silencing-Induced Metaplasticity in Hippocampal Cultured Neurons
Соколова, И. В., & Módy, I. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90378.2008 · Cited by 22
Moss, B. J., Park, L., Dahlberg, C. L., & Juo, P. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006180 · Cited by 22
Steinmetz, C. C., Tatavarty, V., Sugino, K., Shima, Y., Joseph, A., Lin, H. K., Rutlin, M. L., Lambo, M. E., Hempel, C. M., Okaty, B. W., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.08.009 · Cited by 22
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Baek, E., Lee, H., Kim, Y., & Kim, J. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358268 · Cited by 22
Zhu, Y., Uytiepo, M., Bushong, E. A., Haberl, M. G., Beutter, E., Scheiwe, F., Zhang, W., Chang, L., Luu, D., Chui, B., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.108953 · Cited by 22
Emergence of Action Potential Generation and Synaptic Transmission in Vestibular Nucleus Neurons
Shao, M., Hirsch, J. C., & Peusner, K. D. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00180.2006 · Cited by 22
Suzuki, S., Zhou, H., Neumaier, J. F., & Pham, T. A. (2007) · The Journal of Comparative Neurology · doi:10.1002/cne.21424 · Cited by 22
Baertsch, N. A., & Baker‐Herman, T. L. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00018.2013 · Cited by 22
Evolution of Excitation–Inhibition Ratio in Cortical Cultures Exposed to Hypoxia
le Feber, J., Dummer, A., Hassink, G. C., van Putten, M. J. A. M., & Hofmeijer, J. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00183 · Cited by 22
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Louros, S. R., Caldeira, G. L., & Carvalho, A. L. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00328 · Cited by 22
A technique for repeated recordings in cortical organotypic slices
Dong, H., & Buonomano, D. V. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.01.017 · Cited by 22
Braegelmann, K. M., Streeter, K., Fields, D. P., & Baker, T. L. (2016) · Experimental Neurology · doi:10.1016/j.expneurol.2016.07.012 · Cited by 21
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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
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