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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
Flagged first, then by citations
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).
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Vogelstein, R. J. (2007) · Cited by 2
Learning and control with large dynamic neural networks
Daucé, E. (2007) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2007-00060-8 · Cited by 2
Kesselring, J., Comı, G., & Thompson, A. J. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511781698.002 · Cited by 2
Triesch, J., & Hafner, A. (2018) · doi:10.26226/morressier.5b31ec402afeeb001345bc52 · Cited by 2
Kozdon, K., & Bentley, P. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00122 · Cited by 2
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Rasiah, N. P., Rosenegger, D. G., Daviu, N., Füzesi, T., Muir, J., Sterley, T.-L., & Bains, J. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.27.064725 · Cited by 2
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Koster, K. P., Flores-Barrera, E., de la Villarmois, E. A., Nguyen, T. T. A., Niqula, A., Noriega-González, L. Y., Fyke, Z., Caballero, A., Cologna, S. M., Tseng, K. Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.16.473002 · Cited by 2
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Pilzak, A., Pennington, B., & Thivierge, J. (2026) · Nature Communications · doi:10.1038/s41467-026-70920-3 · Cited by 1
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Humble, J. (2013) · PEARL (University of Plymouth) · doi:10.24382/2202 · Cited by 1
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Caviglia, A., Marostica, F., Bardini, R., Savino, A., & Carlo, S. D. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.15058 · Cited by 1
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Zheng, W. (2003) · Springer eBooks · doi:10.1007/978-1-4615-0039-1_16 · Cited by 1
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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