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Synaptic plasticity: taming the beast
Abstract
Synaptic plasticity provides the basis for most models of learning, memory and development in neural circuits. To generate realistic results, synapse-specific Hebbian forms of plasticity, such as long-term potentiation and depression, must be augmented by global processes that regulate overall levels of neuronal and network activity. Regulatory processes are often as important as the more intensively studied Hebbian processes in determining the consequences of synaptic plasticity for network function. Recent experimental results suggest several novel mechanisms for regulating levels of activity in conjunction with Hebbian synaptic modification. We review three of them-synaptic scaling, spike-timing dependent plasticity and synaptic redistribution-and discuss their functional implications.
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“Many forms of correlation-based plasticity have now been described biologically, and each is likely to introduce its own unique destabilizing influences on neural circuit function (Abbott and Nelson 2000).”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Abbott and Nelson 2000
In the citing paper
“synapse-specific correlation-based plasticity mechanisms such as long-term potentiation (LTP) and depression (LTD) are widely thought to contribute to learning and information storage, but theoreticians have long appreciated that they generate a powerful destabilizing force on network function (Miller and MacKay 1994;Abbott and Nelson 2000).”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Abbott and Nelson 2000
In the citing paper
“The locus of change during homeostatic plasticity has important consequences for circuit function, because (for example) presynaptic changes in release probability will strongly affect short-term plasticity and thus the dynamics of information transfer across synapses, whereas a postsynaptic change in receptor accumulation can scale postsynaptic responsiveness without affecting the short-term”
From Homeostatic Synaptic Plasticity: Local and Global Mechanisms for Stabilizing Neuronal Function (2012) · cites this paper as Abbott and Nelson 2000
Citations
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“This is consistent with various models of spike timing-dependent plasticity (Abbott and Nelson, 2000; Song and Abbott, 2001).”
ResultMulticontact Co-operativity in Spike-Timing–Dependent Structural Plasticity Stabilizes Networks
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BackgroundAMPA receptor trafficking and its role in heterosynaptic plasticity
Antunes, G., & Simões-de-Souza, F. M. (2018) · Scientific Reports · doi:10.1038/s41598-018-28581-w · Cited by 28
The Synaptic Scaling Literature: A Systematic Review of Methodologies and Quality of Reporting
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“That is a somewhat counterintuitive preference, as the field has long stated the theoretical importance of homeostatic mechanisms for protecting network stability, usually from the effects of excessive activity caused by Hebbian types of plasticity (Abbott and Nelson, 2000; Turrigiano, 2012).”
BackgroundStandage, D., Jalil, S., & Trappenberg, T. P. (2007) · Biological Cybernetics · doi:10.1007/s00422-007-0152-6 · Cited by 25
“synapses (Abbott and Nelson 2000) are still a problem for these rules, at least under parameters suggested by weightdependent STDP data (Bi and Poo 1998).”
BackgroundEvolving Developing Spiking Neural Networks
Federici, D. (2005) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2005.1554730 · Cited by 25
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Skorheim, S. W., Lonjers, P., & Bazhenov, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090821 · Cited by 25
“These include weight-dependence, so that weaker synapses potentiate more while stronger synapses express less potentiation, and in the limit even depress [36,41,42], and/or precise balancing of STDP rules for potentiation and depression [36,43,44,45,46,47,48,49].”
BackgroundBhandari, A., Ward, T. W., SMITH, J., & Hook, M. J. V. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2022.01.029 · Cited by 24
Kim, S., Jo, S. B., Kim, J., Rhee, D., Choi, Y. Y., Kim, D. H., Kang, J., & Cho, J. H. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202111956 · Cited by 24
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“are called synaptic plasticity, which is one of the basic mechanisms involved in the process of learning and memory.(11)”
BackgroundOptoelectronic Synapses Based on MoS2 Transistors for Accurate Image Recognition
Huang, B., Li, N., Wang, Q., Ouyang, C., He, C., Zhang, L., Du, L., Yang, W., Yang, R., Shi, D., et al. (2022) · Advanced Materials Interfaces · doi:10.1002/admi.202201558 · Cited by 24
Fully-primed slowly-recovering vesicles mediate presynaptic LTP at neocortical neurons
Weichard, I., Taschenberger, H., Gsell, F., Bornschein, G., Ritzau‐Jost, A., Schmidt, H., Kittel, R. J., Eilers, J., Neher, E., Hallermann, S., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305460120 · Cited by 24
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Offutt, S. J., Ryan, K. J., Konop, A. E., & Lim, H. H. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/6/066001 · Cited by 24
“…or depress based on timing, we are looking at how electrical and acoustic stimulation facilitate or suppress a population of cells based on timing (Abbott and Nelson, 2000; Bi and Poo, 1998; Caporale and Dan, 2008; Dan and Poo, 2004; Magee and Johnston, 1997; Markram et al., 1997; Yao and Dan,…”
BackgroundSilvanto, J., & Muggleton, N. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.10.022 · Cited by 27
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Multiscale analysis of slow-fast neuronal learning models with noise
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“Another idea to address hierarchy in networks would be to replace the synaptic decay term −κW by another homeostatic term [32] which would enforce the emergence of a strong hierarchical structure.”
BackgroundTriplet Spike Time-Dependent Plasticity in a Floating-Gate Synapse
Gopalakrishnan, R., & Basu, A. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2506740 · Cited by 23
“VER the past ten years, numerous experimental studies [1]–[4] have shown that the synaptic strength varies as a function of the precise spike timing difference t = t post − t pre between the firing times t pre and t post of the presynaptic and postsynaptic neurons, respectively.”
Emulating homeoplasticity phenomena with organic electrochemical devices
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Asymmetric adaptivity induces recurrent synchronization in complex networks
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Homeostatic Synaptic Plasticity
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“Homeostatic synaptic plasticity mechanisms are emerging as important complements to Hebbian forms of plasticity in the activity-dependent refinement of synaptic connectivity (14, 87, 1).”
BackgroundInfluentialBennett, J., & Bair, W. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004422 · Cited by 23
“A bias for weakening in STDP is frequently observed in experiments [14, 64], but is not the only possible source of synaptic competition, which can also be achieved by homeostatic regulation and intrinsic plasticity [65, 66].”
BackgroundGaltier, M. N., & Wainrib, G. (2013) · Neural Computation · doi:10.1162/neco_a_00512 · Cited by 21
“2”
BackgroundInfluentialKourosh‐Arami, M., Sohya, K., Sarihi, A., Jiang, B., Yanagawa, Y., & Tsumoto, T. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5350-12.2013 · Cited by 25
“As pointed out in theoretical studies (Bienenstock et al., 1982; for review, see Abbott and Nelson, 2000), one-way operation of synaptic plasticity, such as homLTP, would lead to saturation of synaptic transmission efficacy.”
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“This has been found to take several forms in different brain regions (Abbott and Nelson, 2000) but here we focus on the form observed in retinotectal connections and neocortical and hippocampal pyramidal cells where pre → post spike pairs lead to synaptic potentiation (with greater effect over…”
BackgroundZylberberg, J., & DeWeese, M. R. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003182 · Cited by 22
“For our LIF neurons, this is similar to synaptic rescaling, which has been proposed as a mechanism to stabilize correlation-based learning schemes [17,18], and has been observed in physiology experiments [17].”
MethodSelf-organization and Emergence of Dynamical Structures in Neuromorphic Atomic Switch Networks
Stieg, A. Z., Avizienis, A. V., Sillin, H. O., Aguilera, R., Shieh, H.-H., Martin‐Olmos, C., Sandouk, E. J., Aono, M., & Gimzewski, J. K. (2013) · Springer eBooks · doi:10.1007/978-3-319-02630-5_10 · Cited by 23
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“It has been demonstrated in various neural circuits over a wide spectrum of species, from insects to humans (Caporale and Dan, 2008), and various types of STDP have been identified (Abbott and Nelson, 2000).”
BackgroundKlos, C., Miner, D. C., & Triesch, J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006187 · Cited by 22
“It additionally demonstrated how homeostatic mechanisms prevent the runaway connection growth and unstable overlearning [16–19] that otherwise tends to occur from STDP alone.”
Kim, S.-Y., & Lim, W. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09580-y · Cited by 24
“This synaptic plasticity provides the basis for learning, memory, and development (Abbott and Nelson 2000).”
BackgroundMethodLong-term depression in the sensorimotor cortex induced by repeated delivery of 10 Hz trains in vivo
Werk, C. M., Klein, H. S., Nesbitt, C., & Chapman, C. A. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2006.02.004 · Cited by 23
“Long-term potentiation (LTP) is a strengthening of synaptic connections that can be induced by tetanic stimulation (Bliss and Collingridge, 1993; Malenka and Nicoll, 1999), and long-term depression (LTD) is a weakening of synaptic strength that is typically induced by prolonged low-frequency stimulation (Christie et a…”
Long‐term synaptic depression in the adult entorhinal cortex in vivo
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“Mechanisms of longterm depression (LTD) of synaptic transmission may moderate cortical excitability and contribute to memory formation by modifying connections within neuronal networks (Christie et al., 1994; Bear, 1996; Abbott and Nelson, 2000; Braunewell and Manahan-Vaughan, 2001).”
BackgroundAn asynchronous spiking chaotic neuron integrated circuit
Horio, Y., Taniguchi, T., & Aihara, K. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.09.001 · Cited by 23
Gain adjustment of inhibitory synapses in the auditory system
Kotak, V. C., & Sanes, D. H. (2003) · Biological Cybernetics · doi:10.1007/s00422-003-0441-7 · Cited by 23
“There have been many recent experimental and modeling studies that address this concept in vitro (Abbott and Nelson 2000) , but its application to in vivo coding properties remains to be studied in simple systems such as the LSO.”
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“Equation (19) involves the product of β , , −”
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
“Preserving the delicate balance between stability and adaptability, neurons use various powerful mechanisms to stabilize firing rates at defined set-point values in response to bidirectional perturbations of the network activity (Turrigiano et al., 1998; Abbott and Nelson, 2000; Burrone et al., 2002; Desai et al., 200…”
BackgroundHan, H., Ge, F., Ma, M., Yu, H., Wei, H., Zhao, X., Yao, H., Gong, J., Qiu, L., & Xu, W. (2020) · Nanoscale Horizons · doi:10.1039/d0nh00348d · Cited by 22
Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks
Lameu, E. L., Borges, F. S., Iarosz, K. C., Protachevicz, P. R., Antonopoulos, C. G., Macau, E. E. N., & Batista, A. M. (2020) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2020.105689 · Cited by 22
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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
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Synaptic plasticity in inhibitory neurons of the auditory brainstem
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“This idea reflects a long-standing issue in the plasticity field: e.g., LTP naturally begets more LTP, eventually driving synapses, and cellular excitability, to saturation (Abbott and Nelson, 2000).”
BackgroundA VLSI network of spiking neurons with an asynchronous static random access memory
Moradi, S., & Indiveri, G. (2011) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2011.6107781 · Cited by 22
Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network
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Synaptic plasticity and spike-based computation in VLSI networks of integrate-and-fire neurons
Indiveri, G. (2007) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-93194 · Cited by 22
“The type of plasticity that we consider here is “long-term” plasticity [1].”
BackgroundLow-voltage protonic/electronic hybrid indium zinc oxide synaptic transistors on paper substrates
Wu, G., Wan, C., Zhou, J., Zhu, L. Q., & Wan, Q. C. (2014) · Nanotechnology · doi:10.1088/0957-4484/25/9/094001 · Cited by 22
Causal entropies—A measure for determining changes in the temporal organization of neural systems
Waddell, J. N., Dzakpasu, R., Booth, V., Riley, B. T., Reasor, J. D., Poe, G. R., & Żochowski, M. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.12.008 · Cited by 22
“…synaptic modifications, long term potentiation (LTP) and depotentiation (DP), where the direction of the synaptic modification (i.e. strengthening or weakening of the synapse) is directly related to the relative timing of the pre- and post-synaptic spikes (for example see Abbot & Nelson, 2003).”
BackgroundShifting attention to dynamics: Self-reconfiguration of neural networks
Kirst, C., Modes, C. D., & Magnasco, M. O. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.04.006 · Cited by 23
Natural Selection in the Brain
Fernando, C., & Szathmáry, E. (2009) · On thinking · doi:10.1007/978-3-642-03129-8_20 · Cited by 22
“authors ( Abbott and Nelson 2000; Bi and Poo 2001; Florian 2007; Gerstner and Kistler 2002).”
BackgroundBhasin, B. J., Raymond, J. L., & Goldman, M. S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406010121 · Cited by 21
Antunes, G., da Silva, S. F. F., & de Souza, F. M. S. (2017) · International Journal of Neural Systems · doi:10.1142/s0129065717500587 · Cited by 21
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Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems
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“In this approach, Spike Timing Dependent Plasticity (STDP) [32,33] together with Bienenstock, Cooper, and Munro’s (BCM) learning rule [34,35] are combined to develop the BCM-STDP rule [36,37].”
MethodIntrinsic plasticity and birdsong learning
Daou, A., & Margoliash, D. (2021) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2021.107407 · Cited by 24
“dependent changes in synaptic strength as a mechanism for information storage (Abbott and Nelson, 2000; Malenka and Nicoll, 1999).”
BackgroundIGF-1 receptor regulates upward firing rate homeostasis via the mitochondrial calcium uniporter
Katsenelson, M., Shapira, I., Abbas, E., Jevdokimenko, K., Styr, B., Ruggiero, A., Aïd, S., Fornasiero, E. F., Holzenberger, M., Rizzoli, S. O., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2121040119 · Cited by 23
Pariz, A., Trotter, D., Hutt, A., & Lefebvre, J. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010736 · Cited by 23
“To avoid biased synaptic changes (i.e. preferential LTP/LTD), we chose a symmetric STDP Hebbian learning rule [58, 59].”
BackgroundMethodInfluentialPetanjek, Z., Banovac, I., Sedmak, D., & Hladnik, A. (2023) · Advances in neurobiology · doi:10.1007/978-3-031-36159-3_4 · Cited by 34
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“…cell are scaled down by a common factor based on the postsynaptic cell’s recent activity and has its basis in the synaptic scaling literature (Abbott & Nelson, 2000; Turrigiano & Nelson, 2000; Feldman, 2002); in addition, it has been found that even short periods of adaptation cause a…”
BackgroundJacobs, S. A., Cheng, C., & Doering, L. C. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.03.011 · Cited by 25
“Spiny stellate neurons of the hippocampus are often excitatory in nature (Abbott and Nelson, 2000).”
BackgroundFélix-Oliveira, A., Dias, R. B., Colino-Oliveira, M., Rombo, D. M., & Sebastião, A. M. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00058.2014 · Cited by 20
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Oxide-based memristive neuromorphic synaptic devices
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Long-Lasting Desynchronization of Plastic Neural Networks by Random Reset Stimulation
Khaledi‐Nasab, A., Kromer, J. A., & Tass, P. A. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2020.622620 · Cited by 21
“A prominent mechanism leading to adaptive connectivity is spike-timing dependent plasticity (STDP), which modifies the coupling strengths based on the relative timing of post- and presynaptic spikes (Markram et al., 1997; Abbott and Nelson, 2000; Caporale and Dan, 2008).”
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“First, motor learning is typically accompanied by activity-dependent modifications of synapses inducing Hebbian plasticity in the form of long term potentiation-like or long term depression-like changes within cortical neurons (Abbott and Nelson, 2000; Muellbacher et al., 2002).”
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“Computational models of activity-dependent map formation have traditionally employed a learning rule in which synapses between two neurons are strengthened in proportion to the degree of correlation in their firing (Abbott and Nelson, 2000).”
Ursino, M., Cuppini, C., & Magosso, E. (2011) · Cognitive Neurodynamics · doi:10.1007/s11571-011-9154-0 · Cited by 21
“Furthermore, gamma-band synchronization agrees with recent temporal rules of Hebbian potentiation (Abbott and Nelson 2000; Markram et al. 1997).”
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“We focused on a learning rule for neural networks termed STDP, which is a temporally asymmetric learning rule recently found to occur in the cerebral cortex and the hippocampus [22, 23].”
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“Exploring STDP with this software-based approach has advantages over a hardware implementation in that the implementation time is shorter and testing is easier, since no new hardware has to be added on chip and all of the algorithm’s variables are accessible.”
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2D MoTe 2 memristors for energy-efficient artificial synapses and neuromorphic applications
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“destabilizing force on neural circuitry, making neurons and networks vulnerable to synaptic saturation or synaptic silencing through positive feedback mechanisms (Miller, 1996; Abbott and Nelson, 2000; Turrigiano, 2008; Turrigiano, 2012).”
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Pairwise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex
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“It has long been thought that plasticity mechanisms cause networks to destabilize (Abbott and Nelson, 2000), and many cellular and network mechanisms, generally termed as homeostatic plasticity, have been discovered to push their state back to baseline (Turrigiano, 2012).”
BackgroundKhaledi‐Nasab, A., Kromer, J. A., & Tass, P. A. (2022) · Frontiers in Network Physiology · doi:10.3389/fnetp.2022.864859 · Cited by 20
“One of these plasticity mechanisms is spike-timing-dependent plasticity (STDP), where the synaptic strengths are modulated according to the relative timings of postand presynaptic spikes (Markram et al., 1997; Abbott and Nelson, 2000; Caporale and Dan, 2008).”
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Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity
Duchet, B., Bick, C., & Byrne, Á. (2023) · Neural Computation · doi:10.1162/neco_a_01601 · Cited by 22
“Non-causal, symmetric STDP has also been reported in the hippocampus (Abbott & Nelson, 2000; Mishra, Kim, Guzman, & Jonas, 2016), see Fig.”
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Karabanov, A., Ziemann, U., Hamada, M., George, M. S., Quartarone, A., Classen, J., Massimini, M., Rothwell, J., & Siebner, H. R. (2015) · Brain Stimulation · doi:10.1016/j.brs.2015.06.016 · Cited by 5
Metadata
- Authors
- Abbott, L. F., & Nelson, S. B.
- Affiliations
- Brandeis University (US)
- Corresponding author
- Abbott, L. F.
- Venue
- Nature Neuroscience
- Published
- 2000-11
- Volume
- 3
- Issue
- S11
- Pages
- 1178-1183
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W1489333352
- Cited by
- 2,263
- References
- 51
- Citations by year
- 2026: 87 · 2025: 129 · 2024: 121 · 2023: 125 · 2022: 122 · 2021: 129 · 2020: 137 · 2019: 103 · 2018: 121 · 2017: 89 · 2016: 94 · 2015: 90 · 2014: 91 · 2013: 82 · 2012: 86
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ey011116-07)
- National Institute of Mental Health (R01MH058754, MH58754)
- National Eye Institute (EY11116)
- 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, Hebbian plasticity, long-term potentiation, long-term depression, synaptic scaling, spike-timing-dependent plasticity, learning and memory, neural development
- MeSH terms
- Action Potentials, Animals, Humans, Models, Neurological, Nerve Net, Synaptic Transmission, Neuronal Plasticity, Neurons, Synapses, Receptors, Glutamate, Long-Term Potentiation
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