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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).
Cited by
Sorted by citations
Items 1,501–1,600 of 2,247
Reuveni, I., Ghosh, S., & Barkai, E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005306 · Cited by 9
A Computational Model of Neuroreceptor-Dependent Plasticity (NRDP) Based on Spiking Neural Networks
Ramos, J. I. E., Capecci, E., & Kasabov, N. K. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2776863 · Cited by 9
Toossi, H., del Cid‐Pellitero, E., & Jones, B. E. (2017) · eNeuro · doi:10.1523/eneuro.0269-17.2017 · Cited by 9
Preservation of global synaptic excitatory to inhibitory ratio during long postmortem intervals
Scaduto, P., Sequeira, A., Vawter, M. P., Bunney, W. E., & Limón, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-65377-3 · Cited by 9
Wang, Q., Mergia, E., Koesling, D., & Mittmann, T. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2020.7364 · Cited by 9
Widrow, B. C., Kim, Y., Park, D., & Perin, J. K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-815480-9.00001-3 · Cited by 9
Cramer, B., Kreft, M., Billaudelle, S., Karasenko, V., Leibfried, A., Müller, E., Spilger, P., Weis, J., Schemmel, J., Muñoz, M. A., et al. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033035 · Cited by 9
Lin, H., Hangya, B., Fox, S. E., & Muller, R. U. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4900-11.2012 · Cited by 9
Hohnke, C. D., & Sur, M. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-01-00236.1999 · Cited by 9
Synaptic Mechanisms of Activity-Dependent Remodeling in Visual Cortex during Monocular Deprivation
Rittenhouse, C. D., & Majewska, A. K. (2009) · Journal of Experimental Neuroscience · doi:10.4137/jen.s2559 · Cited by 9
Long-lasting changes in neural networks to compensate for altered nicotinic input
John, D., & Berg, D. K. (2015) · Biochemical Pharmacology · doi:10.1016/j.bcp.2015.07.020 · Cited by 9
Adaptive noise shaping neural spike encoding and decoding
Shin, J. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00452-0 · Cited by 9
A recipe for ridding synapses of the ubiquitous AMPA receptor
Turrigiano, G. G. (2002) · Trends in Neurosciences · doi:10.1016/s0166-2236(02)02292-0 · Cited by 9
Self-citationZhu, L., Lai, Y., Hoppensteadt, F. C., & He, J. (2006) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2189969 · Cited by 9
Plastic-Adaptive Properties of Cortical Areas
Dinse, H. R., & Boehmer, G. (2010) · Taylor & Francis eBooks · doi:10.4324/9780203219911_chapter_14 · Cited by 9
Receptor clustering: Activate to accumulate?
Kamboj, S. K., & Huganir, R. L. (1998) · Current Biology · doi:10.1016/s0960-9822(98)70457-9 · Cited by 9
Kitano, K., & Fukai, T. (2004) · Learning & Memory · doi:10.1101/lm.64904 · Cited by 9
A Mathematical Approach to Self-Organized Criticality in Neural Networks
Levina, A. (2008) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-2496 · Cited by 9
Sleep-Wake and Cortical Synaptic Plasticity
Timofeev, I., & Chauvette, S. (2019) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-813743-7.00029-3 · Cited by 8
Karbowski, J., & Urban, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-49321-9 · Cited by 9
Saunders, S. E., & Santin, J. M. (2024) · BMC Biology · doi:10.1186/s12915-024-02050-5 · Cited by 8
Postsynaptic Deregulation in GAP-43 Heterozygous Mouse Barrel Cortex
Kelly, E. A., Tremblay, M., McCasland, J. S., & Majewska, A. K. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp231 · Cited by 8
Biological Modeling in the Discovery and Validation of Cognitive Dysfunctions Biomarkers
Iris, F. (2008) · Springer eBooks · doi:10.1007/978-0-387-79251-4_19 · Cited by 8
Theta Priming of 1-Hz rTMS in Healthy Volunteers
Tallabs, F. A., & Hammond-Tooke, G. D. (2013) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e31827ed0e3 · Cited by 8
Regulation of neural differentiation, synaptic scaling and animal behavior by MeCP2 phophorylation
Zhong, X., Li, H., Kim, J., & Chang, Q. (2018) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2018.04.014 · Cited by 8
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Jha, U., Kondrychyn, I., Korzh, V. P., & Thirumalai, V. (2021) · eNeuro · doi:10.1523/eneuro.0493-20.2021 · Cited by 8
Kleidonas, D., & Vlachos, A. (2021) · Cells · doi:10.3390/cells10113232 · Cited by 8
Wong, N. F., & Xu‐Friedman, M. A. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1583-21.2022 · Cited by 8
Bar, L., Shalom, L., Lezmy, J., Peretz, A. S., & Attali, B. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.972023 · Cited by 8
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Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2023) · eLife · doi:10.7554/elife.92595 · Cited by 8
Sugimura, T., Yoshimura, Y., & Komatsu, Y. (2015) · Neuroscience Research · doi:10.1016/j.neures.2015.02.005 · Cited by 8
Delgado, J. Y., & Owens, G. C. (2012) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2012.00031 · Cited by 8
Systematic Data Mining of Hippocampal Synaptic Properties
Moradi, K., & Ascoli, G. A. (2018) · Springer series in computational neuroscience · doi:10.1007/978-3-319-99103-0_11 · Cited by 8
Synaptic plasticity onto inhibitory neurons as a mechanism for ocular dominance plasticity
Bono, J., & Clopath, C. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006834 · Cited by 8
Classification of Visual Cortex Plasticity Phenotypes following Treatment for Amblyopia
Balsor, J. L., Jones, D. G., & Murphy, K. M. (2019) · Neural Plasticity · doi:10.1155/2019/2564018 · Cited by 8
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Schubert, F., & Gros, C. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.587721 · Cited by 8
Roux, N. L., Amar, M., & Fossier, P. (2008) · Journal de la Société de Biologie · doi:10.1051/jbio:2008018 · Cited by 8
Blanco, W., & Ribeiro, S. T. G. (2013) · Revista USP · doi:10.11606/issn.2316-9036.v0i98p17-30 · Cited by 8
Timing-Dependent Priming Effects of Anodal tDCS on Two-Hand Coordination
Azarpaikan, A., Taheri, H. R., Sohrabi, M., Boostani, R., & Ghoshoni, M. (2019) · Journal of Psychophysiology · doi:10.1027/0269-8803/a000250 · Cited by 8
Santin, J. M. (2019) · Developmental Neurobiology · doi:10.1002/dneu.22721 · Cited by 8
Razak, K. A., & Pallas, S. L. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1143-07.2007 · Cited by 8
Dual synaptic plasticity in the hippocampus: Hebbian and spatiotemporal learning dynamics
Kaneki, K., Araki, O., & Tsukada, M. (2008) · Cognitive Neurodynamics · doi:10.1007/s11571-008-9071-z · Cited by 8
Inhomogeneities in Heteroassociative Memories with Linear Learning Rules
Sterratt, D. C., & Willshaw, D. J. (2007) · Neural Computation · doi:10.1162/neco.2007.08-06-301 · Cited by 8
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Elliott, T., & Shadbolt, N. R. (2003) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2003.08.001 · Cited by 8
Krueger, J. M., Opp, M. R., Toth, L. A., Johannsen, L., & Kapás, L. (1990) · Current topics in neuroendocrinology · doi:10.1007/978-3-642-75837-9_10 · Cited by 8
Temporal coding in an olfactory oscillatory model
Quenet, B., Horn, D., Dreyfus, G., & Dubois, R. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00469-6 · Cited by 8
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What neuroscience can tell AI about learning in continuously changing environments
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Lenz, M., Kruse, P., Eichler, A., Straehle, J., Hemeling, H., Stöhr, P., Beck, J., & Vlachos, A. (2024) · Brain Communications · doi:10.1093/braincomms/fcae351 · Cited by 7
Homeostatic regulation of brain activity: from endogenous mechanisms to homeostatic nanomachines
Michetti, C., & Benfenati, F. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00470.2024 · Cited by 7
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Vincen‐Brown, M. A., Revill, A. L., & Pilarski, J. Q. (2016) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2016.03.013 · Cited by 7
Quantifying phosphorylation dynamics in primary neuronal cultures using LC-MS/MS
Desch, K., Schuman, E. M., & Langer, J. D. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.101063 · Cited by 7
Chen, K., Beyeler, M., & Krichmar, J. L. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0384-22.2022 · Cited by 7
Chan, E. S., Ge, Y., So, Y. W., Bai, Y., Liu, L., & Wang, Y. T. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111584 · Cited by 7
Nuwer, J. L., Povysheva, N. V., & Jacob, T. C. (2023) · Neuropharmacology · doi:10.1016/j.neuropharm.2023.109587 · Cited by 7
Searching for long timescales without fine tuning
Chen, X., & Bialek, W. (2024) · Physical review. E · doi:10.1103/physreve.110.034407 · Cited by 7
Interaction of short-term depression and firing dynamics in shaping single neuron encoding
Mohan, A., McDonnell, M. D., & Stricker, C. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00041 · Cited by 7
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Rollenhagen, A., & Lübke, J. H. R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-1997-6_11 · Cited by 7
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Hartmann, C., Miner, D. C., & Triesch, J. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00090 · Cited by 7
Strehl, A., Galanis, C., Radic, T., Schwarzacher, S. W., Deller, T., & Vlachos, A. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00303 · Cited by 7
Bove, C., Coleman, F. H., & Travagli, R. A. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.08.048 · Cited by 7
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Wu, L., Bao, L., Wang, Z., Yu, Z., Wang, B., Chen, Q., Ling, Y., Qin, Y., Tang, K., Cai, Y., et al. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202001104 · Cited by 7
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Power, E. M., Ganeshan, D., & Iremonger, K. J. (2023) · Journal of Experimental Biology · doi:10.1242/jeb.245222 · Cited by 7
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Zhou, L., Sun, X., & Duan, J. (2023) · Brain Science Advances · doi:10.26599/bsa.2022.9050020 · Cited by 7
Timofeev, I. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6978-1_8 · Cited by 7
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Casanova, J. R., Nishimura, M., Le, J. P., Lam, T. T., & Swann, J. W. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12315 · Cited by 7
Neural Sensitivity to Odorants in Deprived and Normal Olfactory Bulbs
Rodrı́guez, F. B., Huerta, R., & Aylwin, M. D. L. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060745 · Cited by 7
How REM sleep shapes hypothalamic computations for feeding behavior
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Nargeot, R. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-09-03282.2001 · Cited by 7
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Mo, C., Gu, M., & Koch, C. (2004) · Neural Computation · doi:10.1162/0899766042321788 · Cited by 7
A New Technique to Control the Architecture of Neuronal Networks in vitro
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STUDY OF HUMAN THERMOREGULATION: ADAPTIVE OPTIMIZATION CONTROL THEORY ANALYSIS
Ng, E. Y. K., & LIM, L. W. (2008) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s021951940800253x · Cited by 7
Statistical Traces of Long-Term Memories Stored in Strengths and Patterns of Synaptic Connections
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Homeostatic development of dynamic neural fields
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A spike based learning rule for generation of invariant representations
Kording, K. P., & Peter König, B. (2000) · Journal of Physiology-Paris · doi:10.1016/s0928-4257(00)01088-3 · Cited by 7
Buzsáki, G., Carpi, D., Csicsvari, J., Dragoi, G., Harris, K., Henze, D. A., & Hirase, H. (2003) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198574002.003.0014 · Cited by 7
Blanc, G. (2010) · Cited by 7
Herzog, A., Kube, K., Michaelis, B., de Lima, A. D., Baltz, T., & Voigt, T. B. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.027 · Cited by 7
Learning in large-scale spiking neural networks
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Rose, C. R., & Konnerth, A. (2000) · Nature · doi:10.1038/35013191 · Cited by 7
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Shirani, F., & Choi, H. (2023) · Journal of Computational Neuroscience · doi:10.1007/s10827-023-00863-x · Cited by 6
Zhou, Y.-S., Tao, H., Lv, S.-S., Liang, K.-Q., Shi, W.-Y., Liu, K., Li, Y., Chen, L.-Y., Zhou, L., Yin, S., et al. (2024) · Acta Pharmacologica Sinica · doi:10.1038/s41401-024-01275-y · Cited by 6
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Kareemo, D. J., Winborn, C. S., Olah, S. S., Miller, C. N., Kim, J., Kadgien, C., Actor-Engel, H. S., Ramsay, H. J., Ramsey, A. M., Aoto, J. N., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54530-5 · Cited by 6
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dos Santos, F. P., & Verschure, P. F. M. J. (2025) · Network Neuroscience · doi:10.1162/netn_a_00460 · Cited by 6
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Iatropoulos, G., Wulfram Gerstner, B., & Brea, J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2422602122 · Cited by 6
Development of Motoneuron Electrical Properties and Motor Output
Baines, R. (2006) · International review of neurobiology · doi:10.1016/s0074-7742(06)75005-x · Cited by 6
Long-Term Memory Search across the Visual Brain
Fedurco, M. (2012) · Neural Plasticity · doi:10.1155/2012/392695 · Cited by 6
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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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