The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
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.
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
“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
Cites
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
Advances in neural information processing systems 11
Peter Sollich, A. (1999) · Cited by 3,168
Bienenstock, E., Cooper, L., & Munro, P. (1982) · The Journal of Neuroscience · doi:10.1523/jneurosci.02-01-00032.1982 · Cited by 2,896
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
Song, S., Miller, K. D., & Abbott, L. F. (2000) · Nature Neuroscience · doi:10.1038/78829 · Cited by 2,794
Self-citationSimplified neuron model as a principal component analyzer
Oja, E. (1982) · Journal of Mathematical Biology · doi:10.1007/bf00275687 · Cited by 2,309
Activity-dependent scaling of quantal amplitude in neocortical neurons
Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B. (1998) · Nature · doi:10.1038/36103 · Cited by 2,226
Self-citationSynaptic depression and cortical gain control
Abbott, L. F., Varela, J. A., Sen, K., & Nelson, S. B. (1997) · Science · doi:10.1126/science.275.5297.221 · Cited by 1,690
Self-citationTsodyks, M. V., & Markram, H. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.2.719 · Cited by 1,607
A Synaptically Controlled, Associative Signal for Hebbian Plasticity in Hippocampal Neurons
Magee, J. C., & Johnston, D. (1997) · Science · doi:10.1126/science.275.5297.209 · Cited by 1,340
Research works printed but not linked: 2
- Minai, A. A. & Levy, W. B. Sequence learning in a single trial . INNS World Congress of Neural Networks II, 505–508 (1993).
- Van Rossum, M. C., Bi, B. & Turrigiano, G. G. Learning rules that generate stable synaptic weight distributions. J. Neurosci. (in press).
Cited by
Sorted by citations
Items 501–600 of 999
Perspectives on adaptive dynamical systems
Sawicki, J., Berner, R., Loos, S. A. M., Anvari, M., Bader, R., Barfuß, W., Botta, N., Brede, N., Franović, I., Gauthier, D. J., et al. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0147231 · Cited by 72
Kuzum, D., Jeyasingh, R. G. D., & Wong, H. ‐. P. (2011) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2011.6131643 · Cited by 63
Huyck, C., & Passmore, P. (2013) · Biological Cybernetics · doi:10.1007/s00422-013-0555-5 · Cited by 68
“This activity must support learning dynamics beyond the neuron (Abbott and Nelson 2000); however, this behaviour is not currently well understood.”
BackgroundLearning of Chunking Sequences in Cognition and Behavior
Fonollosa, J., Neftci, E., & Rabinovich, M. (2015) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1004592 · Cited by 59
“The learning in the elementary layer associates one EM with each input pattern presented in the perceptual layer, according to a correlation-based rule with synaptic scaling [40]:”
BackgroundMethodInfluentialZhou, X. Y., Zhao, L., Zhen, W., Lin, Y., Wang, C., Pan, T., Li, L., Du, G., Lu, L., Cao, X., et al. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202001254 · Cited by 64
Artificial Synapse Based on Organic–Inorganic Hybrid Perovskite with Electric and Optical Modulation
Lao, J., Xu, W. J., Jiang, C., Zhong, N., Tian, B., Lin, H., Luo, C., Travaš‐Sejdić, J., Peng, H., & Duan, C. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100291 · Cited by 62
Van der Waals Ferroelectric Semiconductor Field Effect Transistor for In-Memory Computing
Liao, J., Wen, W., Wu, J., Zhou, Y., Hussain, S., Hu, H., Li, J., Liaqat, A., Zhu, H., Jiao, L., et al. (2023) · ACS Nano · doi:10.1021/acsnano.3c01198 · Cited by 62
Sleep restores an optimal computational regime in cortical networks
Xu, Y., Schneider, A. M., Weßel, R., & Hengen, K. B. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01536-9 · Cited by 62
Timing in Cellular Ca2+ Signaling
Boulware, M. J., & Marchant, J. S. (2008) · Current Biology · doi:10.1016/j.cub.2008.07.018 · Cited by 63
“Most notably in STDP, the type and extent of synaptic modification is dependent on both the respective timing (millisecond discrimination, [29]), and ordering of pre- and post-synaptic action potentials [29,30,39].”
Temporal asymmetry in spike timing-dependent synaptic plasticity
Bi, G., & WANG, H. (2002) · Physiology & Behavior · doi:10.1016/s0031-9384(02)00933-2 · Cited by 62
“Besides the typical asymmetric spike-timing window found in synapses between pyramidal neurons, studies in different systems have revealed the diversity of the STDP windows (for recent reviews, see Refs.”
Artificial synapses based on biopolymer electrolyte-coupled SnO 2 nanowire transistors
Gou, G., Sun, J., Qian, C., He, Y., Kong, L., Fu, Y., Dai, G., Yang, J., & Gao, Y. (2016) · Journal of Materials Chemistry C · doi:10.1039/c6tc03731c · Cited by 61
Rathour, R. K., & Narayanan, R. (2012) · The Journal of Physiology · doi:10.1113/jphysiol.2012.239418 · Cited by 63
“…of spikes with respect to the incoming stimuli, both in terms of the arrival time of the postsynaptic potential (Bi & Poo, 1998; Egger et al. 1999; Abbott & Nelson, 2000; Feldman, 2000; Sjostrom et al. 2001; Froemke & Dan, 2002; Caporale & Dan, 2008) and in terms of the phase at which spikes…”
BackgroundComputer Models and Analysis Tools for Neural Microcircuits
Natschläger, T., Markram, H., & Maass, W. (2003) · Springer eBooks · doi:10.1007/978-1-4615-1079-6_9 · Cited by 63
“The current implementation allows for an arbitrary choice for the function L(∆t), since various different shapes of it have been found for different synaptic connections (Abbott and Nelson, 2000).”
BackgroundHomeostasis of Brain Dynamics in Epilepsy: A Feedback Control Systems Perspective of Seizures
Chakravarthy, N., Tsakalis, K. S., Sabesan, S., & Iasemidis, L. D. (2009) · Annals of Biomedical Engineering · doi:10.1007/s10439-008-9625-6 · Cited by 63
Inducing Homeostatic-Like Plasticity in Human Motor Cortex Through Converging Corticocortical Inputs
Pötter‐Nerger, M., Fischer, S., Mastroeni, C., Groppa, S., Deuschl, G., Volkmann, J., Quartarone, A., Münchau, A. M., & Siebner, H. R. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91046.2008 · Cited by 61
Hartmann, C., Lazăr, A., Nessler, B., & Triesch, J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004640 · Cited by 61
“Theoretical studies on neural plasticity, on the other hand, have long argued that homeostasis is needed to “tame the beast” of Hebbian learning by enforcing stable network dynamics [35].”
BackgroundNanoionic Resistive‐Switching Devices
Zhu, X., Lee, S. H., & Lu, W. D. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900184 · Cited by 61
Activation Mechanisms of the NMDA Receptor
VanDongen, A. M. J., & Blanke, M. L. (2008) · Frontiers in neuroscience · doi:10.1201/9781420044157.ch13 · Cited by 61
Colloquium: Multiscale modeling of brain network organization
Presigny, C., & Fallani, F. D. V. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.031002 · Cited by 66
“A basic question in neuroscience is how learning is acquired through Hebbian plasticity, without leading to runaway excitation of the neural synaptic activity (Abbott and Nelson, 2000; Miller and MacKay, 1994; Watt and Desai, 2010).”
BackgroundAnytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework
Zoubi, O. A., Awad, M., & Kasabov, N. K. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.01.001 · Cited by 60
Santarnecchi, E., Momi, D., Sprugnoli, G., Neri, F., Pascual‐Leone, Á., Rossi, A., & Rossi, S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24329 · Cited by 57
Rapid and sustained homeostatic control of presynaptic exocytosis at a central synapse
Delvendahl, I., Kita, K., & Müller, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1909675116 · Cited by 62
Homeostatic plasticity—a presynaptic perspective
Delvendahl, I., & Müller, M. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.10.003 · Cited by 65
Rule, M. E., & O’Leary, T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106692119 · Cited by 71
Zeng, T., Shi, S., Hu, K., Jia, L., Li, B., Sun, K., Su, H., Gu, Y., Xu, X., Song, D., et al. (2024) · Advanced Materials · doi:10.1002/adma.202401021 · Cited by 61
Wang, H., Megill, A., He, K., Kirkwood, A., & Lee, H. (2012) · Neural Plasticity · doi:10.1155/2012/272374 · Cited by 66
PCMO-Based RRAM and NPN Bipolar Selector as Synapse for Energy Efficient STDP
Lashkare, S., Panwar, N., Kumbhare, P., Das, B., & Ganguly, U. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2723503 · Cited by 59
“Duration of V w a v e is in milliseconds so to mimic biological STDP [11], [38], [39].”
MethodFoncelle, A., Mendes, A., Jędrzejewska‐Szmek, J., Valtcheva, S., Berry, H., Blackwell, K. T., & Venance, L. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00049 · Cited by 67
“STDP has emerged as a candidate mechanism for experience- and activity-dependent changes in neural circuits, including map plasticity (Abbott and Nelson, 2000; Dan and Poo, 2006; Morrison et al., 2008; Sjöström et al., 2008; Feldman, 2012; Froemke, 2015).”
Spike-Timing Dependent Plasticity for Evolved Robots
Paolo, E. A. D. (2002) · Adaptive Behavior · doi:10.1177/1059712302010003006 · Cited by 59
Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation
Zhang, T., Jia, S., Cheng, X., & Xu, B. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3085966 · Cited by 62
“[23], the long-term depression (LTD) [24], and the lateral inhibition [25], [26].”
BackgroundGong, J., Wei, H., Ni, Y., Zhang, S., Du, Y., & Xu, W. (2021) · Materials Today Physics · doi:10.1016/j.mtphys.2021.100540 · Cited by 58
ZnO nanowire optoelectronic synapse for neuromorphic computing
Shen, C., Gao, X., Chen, C., Ren, S., Xu, J., Xia, Y., & Wang, S. (2021) · Nanotechnology · doi:10.1088/1361-6528/ac3687 · Cited by 61
“STDP is known as a representative form of long-term synaptic plasticity ( LTP ) [ 43 ] , based on which synaptic connections can be strengthened or weakened depending on the sequence and timing of the presynaptic and postsynaptic stimuli [ 44 – 46 ] .”
Activity Regulates the Incidence of Heteronymous Sensory-Motor Connections
Mendelsohn, A. I., Simon, C. M., Abbott, L. F., Mentis, G. Z., & Jessell, T. M. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.045 · Cited by 58
“…whether these experimental observations can be accounted for by a differential spike-timing model that captures the distinct temporal features of convergent homonymous and heteronymous sensory inputs onto an individual postsynaptic motor neuron (Figure 7A; Abbott and Nelson, 2000; Feldman, 2012).”
BackgroundSelf-citationRobust development of synfire chains from multiple plasticity mechanisms
Zheng, P., & Triesch, J. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00066 · Cited by 61
“This is achieved by scaling the synapses multiplicatively (Turrigiano et al., 1998; Abbott and Nelson, 2000): A homeostatic (intrinsic) plasticity rule maintains a constant average firing rate in every excitatory neuron, where η IP is the adaption rate and the target firing rates In terms of firing…”
BackgroundResultSynaptic heterogeneity between mouse paracapsular intercalated neurons of the amygdala
Geracitano, R., Kaufmann, W. A., Szabó, G., Ferraguti, F., & Capogna, M. (2007) · The Journal of Physiology · doi:10.1113/jphysiol.2007.142570 · Cited by 59
“At the level of a single synapse, the lack of reliability offers a large dynamic range so that an increase of release probability, generated for example by action potentials occurring at high frequency, enhances synaptic efficacy (Abbott & Nelson, 2000; Thomson, 2000; Zucker & Regehr, 2002).”
BackgroundTHE SUPER-TURING COMPUTATIONAL POWER OF PLASTIC RECURRENT NEURAL NETWORKS
Cabessa, J., & Siegelmann, H. T. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500294 · Cited by 57
“In this sense, they provide a theoretical complement to the numerous experimental studies on the importance of the general mechanism of plasticity in the brain’s information processing.(1,26,27)”
BackgroundNoise-enhanced coupling between two oscillators with long-term plasticity
Lücken, L., Popovych, O. V., Tass, P. A., & Yanchuk, S. (2016) · Physical review. E · doi:10.1103/physreve.93.032210 · Cited by 60
“…and Medicine - Neuromodulation, Jülich Research Center, Jülich, Germany 3Department of Neurosurgery, Stanford University, Stanford, California, USA 4Department of Neuromodulation, University of Cologne, Cologne, Germany 5Institute of Mathematics, Technical University of Berlin,…”
BackgroundBilayered Oxide‐Based Cognitive Memristor with Brain‐Inspired Learning Activities
Xiong, W., Zhu, L. Q., Ye, C., Yu, F., Ren, Z. Y., & Ge, Z. Y. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900439 · Cited by 59
Perspective on Nanofluidic Memristors: From Mechanism to Application
Xie, B., Xiong, T., Li, W., Gao, T., Zong, J., Liu, Y., & Yu, P. (2022) · Chemistry - An Asian Journal · doi:10.1002/asia.202200682 · Cited by 57
Galindo‐Charles, L., Hernández‐López, S., Galarraga, E., Tapia, D., Bargas, J., Garduño, J., Frías‐Dominguez, C., Drucker-Colı́n, R., & Mihailescu, S. (2008) · Synapse · doi:10.1002/syn.20526 · Cited by 58
A framework for plasticity implementation on the SpiNNaker neural architecture
Galluppi, F., Lagorce, X., Stromatias, E., Pfeiffer, M., Plana, L. A., Furber, S. B., & Benosman, R. B. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00429 · Cited by 63
“of learning rules in nature, depending on species, receptor, and neuron types (Abbott and Nelson, 2000; Kullmann et al., 2012), the presence or absence of neuromodulators (Pawlak et al., 2010; Cassenaer and Laurent, 2012), but also on other factors like postsynaptic membrane potential, position on the dendritic arbor,…”
BackgroundInfluentialPairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity
Babadi, B., & Abbott, L. F. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002906 · Cited by 61
“The generally antisymmetric shape of the STDP window, in which reversing the ordering of pre- and postsynaptic spikes reverses the direction of synaptic change, led to the proposal that this synaptic modification rule should eliminate strong recurrent connections between neurons [11,23].”
BackgroundSelf-citationSynaptic organic transistors with a vacuum-deposited charge-trapping nanosheet
Kim, C., Sung, S., & Yoon, M. (2016) · Scientific Reports · doi:10.1038/srep33355 · Cited by 57
Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage
Sgritta, M., Locatelli, F., Soda, T., Prestori, F., & D'Angelo, E. U. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2079-16.2016 · Cited by 58
“…plasticity that exploits the temporal relationship of activity in presynaptic and postsynaptic neurons to regulate the switch between potentiation and depression (called here st-LTP and st-LTD) (Artola and Singer, 1993; Abbott and Nelson, 2000; Caporale and Dan, 2008; Markram et al., 2011).”
BackgroundAsl, M. M., Valizadeh, A., & Tass, P. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-30565-9 · Cited by 56
Adult spinal motoneurons change their neurotransmitter phenotype to control locomotion
Bertuzzi, M., Chang, W., & Ampatzis, K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1809050115 · Cited by 57
Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications
Li, C., Xiong, T., Yu, P., Fei, J., & Mao, L. (2020) · ACS Applied Bio Materials · doi:10.1021/acsabm.0c00806 · Cited by 57
Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning
Gillett, M., Pereira, U., & Brunel, N. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1918674117 · Cited by 58
Memristive Ion Dynamics to Enable Biorealistic Computing
Zhao, R., Kim, S. J., Xu, Y., Zhao, J., Wang, T., Midya, R., Ganguli, S., Roy, A. K., Dubey, M., Williams, R. S., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00587 · Cited by 57
Neuronal Chains for Actions in the Parietal Lobe: A Computational Model
Chersi, F., Ferrari, P. F., & Fogassi, L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027652 · Cited by 55
“The implemented learning rule is the spike-timing dependent plasticity (STDP) [36,58–60], with dynamic synaptic plasticity.”
BackgroundSolitary states in adaptive nonlocal oscillator networks
Berner, R., Polanska, A., Schöll, E., & Yanchuk, S. (2020) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2020-900253-0 · Cited by 54
Gainey, M. A., Tatavarty, V., Nahmani, M., Lin, H. K., & Turrigiano, G. G. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510754112 · Cited by 55
A theoretical network model to analyse neurogenesis and synaptogenesis in the dentate gyrus
Butz, M., Lehmann, K., Dammasch, I. E., & Teuchert, G. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.07.007 · Cited by 59
Chersi, F., Mirolli, M., Pezzulo, G., & Baldassarre, G. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.11.009 · Cited by 57
“The implemented learning rule is the combination of two components: the first is spike-timingdependent plasticity (STDP) (Abbott & Nelson, 2000; Bi & Poo, 2001; Kepecs, van Rossum, Song, & Tegner, 2002; Song, Miller, & Abbott, 2000) and the second is eligibility traces (Izhekevich, 2007; Singh & Sutton, 1996).”
BackgroundThe Interaction of Rhinal Cortex and Hippocampus in Human Declarative Memory Formation
Fell, J., Klaver, P., Elger, C. E., & Fernández, G. (2002) · Reviews in the Neurosciences · doi:10.1515/revneuro.2002.13.4.299 · Cited by 55
Janzakova, K., Ghazal, M., Kumar, A., Coffinier, Y., Pecqueur, S., & Alibart, F. (2021) · Advanced Science · doi:10.1002/advs.202102973 · Cited by 58
Sakurai, A., & Katz, P. S. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-34-10745.2003 · Cited by 54
“The spike timing dependence of DSI neuromodulatory actions resembles spike timing-dependent plasticity (STDP) described at cortical synapses that can lead to a bidirectional change in synaptic strength (Markram et al., 1997; Abbott and Nelson, 2000; Bi and Poo, 2001; Froemke and Dan, 2002); however, unlike STDP, the r…”
BackgroundResultInfluentialOn the role of astroglial syncytia in self-repairing spiking neural networks
Naeem, M., Mcdaid, L. J., Harkin, J., Wade, J. J., & Marsland, J. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2382334 · Cited by 55
“This signaling pathway acts to modulate the transmission probability of the synapse and is a potential candidate for selfrepair of damaged or low-probability synapses [7], [8], [17].”
BackgroundTheta‐burst stimulation over primary motor cortex degrades early motor learning
Iezzi, E., Suppa, A., Conte, A., Agostino, R., Nardella, A., & Berardelli, A. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07090.x · Cited by 54
“Homeostatic plasticity implies mechanisms needed to maintain overall excitability within a dynamic range, otherwise unrestricted plasticity will prevent further synaptic changes (Abraham & Tate, 1997; Abbott & Nelson, 2000; Turrigiano & Nelson, 2004; Davis, 2006).”
BackgroundHassanzahraee, M., Zoghi, M., & Jaberzadeh, S. J. (2018) · Reviews in the Neurosciences · doi:10.1515/revneuro-2017-0111 · Cited by 52
Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing
Wang, Y., Zhang, Z., Xu, M., Yang, Y., Ma, M., Li, H., Pei, J., & Shi, L. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b04901 · Cited by 55
Thomson, A. C., & Sack, A. T. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.599918 · Cited by 60
“This is because continuously firing synapses could only become stronger (driven to saturation) and unused synapses quiescent (until completely lost) (14).”
BackgroundThe Interplay Between Homeostatic Synaptic Plasticity and Functional Dendritic Compartments
Rabinowitch, I., & Segev, I. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00074.2006 · Cited by 55
Hyperexcitability and homeostasis in fragile X Syndrome.
Liu, X., Kumar, V., Tsai, N.-P., & Auerbach, B. D. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.805929 · Cited by 53
“attenuate the hypo- or hyperexcitability owing to uncontrolled LTD or LTP, respectively, robust destabilizing forces in a circuit could pose devastating consequences on network activity (Abbott and Nelson, 2000).”
BackgroundHow Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase
Todri‐Sanial, A., Carapezzi, S., Delacour, C., Abernot, M., Gil, T., Corti, E., Karg, S., Núñez, J., Jiménez, M., Avedillo, M. J., et al. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3107771 · Cited by 57
“We apply Hebbian learning to ONN, which is one of the most commonly used learning rules based on the dynamics of biological systems [41], [42].”
MethodSynaptic Dysfunction in Multiple Sclerosis: A Red Thread from Inflammation to Network Disconnection
Bellingacci, L., Mancini, A., Gaetani, L., Tozzi, A., Parnetti, L., & Filippo, M. D. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189753 · Cited by 53
“Significant LTD changes in EAE animals, however, were demonstrated at the level of superior colliculus (SC) and cerebellum during the acute phase of the experimental disease [83].”
BackgroundMeta-learning synaptic plasticity and memory addressing for continual familiarity detection
Tyulmankov, D., Yang, G. R., & Abbott, L. F. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.009 · Cited by 55
Self-citationDisinhibitory Circuitry Gates Associative Synaptic Plasticity in Olfactory Cortex
Canto-Bustos, M., Friason, F. K., Bassi, C. D., & Oswald, A.-M. M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1369-21.2021 · Cited by 55
Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting
Dodda, A., Jayachandran, D., Radhakrishnan, S. S., Pannone, A., Zhang, Y., Trainor, N., Redwing, J. M., & Das, S. (2022) · ACS Nano · doi:10.1021/acsnano.2c02906 · Cited by 53
Interaction Between Simultaneously Applied Neuromodulatory Interventions in Humans
Schabrun, S. M., Chipchase, L. S., Zipf, N., Thickbroom, G. W., & Hodges, P. W. (2012) · Brain stimulation · doi:10.1016/j.brs.2012.09.009 · Cited by 53
“If synaptic efficacy is uncontrollably increased or decreased by concurrent tDCS/NMES, such mechanisms could destabilize cortical neuronal networks and disrupt further dynamic modifications [37].”
Heba, S., Puts, N. A. J., Kalisch, T., Glaubitz, B., Haag, L. M., Lenz, M., Dinse, H. R., Edden, R. A. E., Tegenthoff, M., & Schmidt‐Wilcke, T. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv296 · Cited by 57
“Cellular studies suggest that changes in synaptic transmission are characterized by a complex balance between inhibition and excitation (Abbott and Nelson 2000; Dorrn et al. 2010; Carcea and Froemke 2013).”
BackgroundPeineau, S., Rabiant, K., Pierrefiche, O., & Potier, B. (2018) · Pharmacological Research · doi:10.1016/j.phrs.2018.01.018 · Cited by 56
“Similarly, continuous synaptic depression during LTD could result in unnecessary synaptic silencing and elimination [61,62].”
BackgroundLi, M., Xiong, Z., Shao, S., Shao, L., Han, S., Wang, H., & Zhao, J. (2021) · Carbon · doi:10.1016/j.carbon.2021.02.046 · Cited by 54
Adaptive Latent Inhibition in Associatively Responsive Optoelectronic Synapse
Ng, S. E., Yang, J., John, R. A., & Mathews, N. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202100807 · Cited by 53
Role of Efficient Neurotransmitter Release in Barrel Map Development
Lu, H., Butts, D. A., Kaeser, P. S., She, W.-C., Janz, R., & Crair, M. C. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.3956-05.2006 · Cited by 55
“7B) (Abbott and Nelson, 2000; Dan and Poo, 2004) similar to that known to drive synaptic development in layer IV to layer II/III synapses in somatosensory cortex (Feldman, 2000) and retinotopic map development at the retinotectal synapse (Zhang et al.”
Duality of Rate Coding and Temporal Coding in Multilayered Feedforward Networks
Masuda, N., & Aihara, K. (2003) · Neural Computation · doi:10.1162/089976603321043711 · Cited by 51
“…Frotscher, & Sakmann, 1997; Abbott & Nelson, 2000), effective connectivity (Aertsen et al., 1989; Fujii et al., 1996), redistribution of synaptic efficacy (Markram & Tsodyks, 1996; Abbott & Nelson, 2000), and synaptic depression (Abbott, Varela, Sen, & Nelson, 1997; Tsodyks & Markram, 1997).”
BackgroundSmolen, P., Baxter, D. A., & Byrne, J. H. (2019) · Learning & Memory · doi:10.1101/lm.049395.119 · Cited by 56
“…its absence, given protein turnover time scales ofminutes to days (Eden et al. 2011), it is likely that passive relaxation processes, and ongoing homeostatic processes (Turrigiano et al. 1998; Abbott and Nelson 2000), would normalize synaptic strengths to an average value, erasing LTM, LTP, or LTF.”
BackgroundWu, T., Zhang, X., & Liu, Z. (2022) · Frontiers of Physics · doi:10.1007/s11467-022-1161-6 · Cited by 53
“On the other hand, plasticity is also one of the most astonishing features of the brain and can be defined as the ability to modify the structural and functional properties of synapses [4, 5].”
BackgroundAltered brain activity in unipolar depression unveiled using connectomics
Cash, R. F. H., Müller, V. I., Fitzgerald, P. B., Eickhoff, S. B., & Zalesky, A. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00038-8 · Cited by 49
Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era
Li, J., Abbas, H., Ang, D. S., Ali, A., & Ju, X. (2023) · Nanoscale Horizons · doi:10.1039/d3nh00180f · Cited by 52
Cortical reactivations predict future sensory responses
Nguyen, N. D., Lutas, A., Amsalem, O., Fernando, J., Ahn, A. Y.-E., Hakim, R., Vergara, J., McMahon, J., Dimidschstein, J., Sabatini, B. L., et al. (2023) · Nature · doi:10.1038/s41586-023-06810-1 · Cited by 53
“These stimulus reactivations accurately predict future stimulus-evoked responses over the course of several trials and the accumulated drift in response patterns over a session at rates proportional to the reactivation rate (Fig.”
Homeostatic structural plasticity increases the efficiency of small-world networks
Butz, M., Steenbuck, I. D., & van Ooyen, A. (2014) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2014.00007 · Cited by 53
“Homeostasis in electrical activity is a ubiquitous principle in the nervous system (Wolff and Wagner, 1983; Ramakers et al., 1991; Abbott and Nelson, 2000) and a variety of plasticity mechanisms can act homeostatically.”
BackgroundDesai, T. R., Dongale, T. D., Patil, S. R., Tiwari, A. P., Pawar, P. K., Kamat, R. K., & Kim, T. G. (2021) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2021.01.108 · Cited by 53
Resonance or Integration? Self-Sustained Dynamics and Excitability of Neural Microcircuits
Muresan, R. C., & Savin, C. (2006) · Journal of Neurophysiology · doi:10.1152/jn.01043.2006 · Cited by 52
“Spontaneous miniature synaptic events and noncortical sources of excitation provide a " lower bound " for cortical activity, preventing it from " dying out.”
BackgroundFregni, F., Boggio, P. S., Valle, A. C., Otachi, P. T. M., Thut, G., Rigonatti, S. P., Marcolin, M. A., Fecteau, S., Pascual‐Leone, Á., Fiore, L. A., et al. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.02.015 · Cited by 52
“It is conceivable that this increase in the CS excitability after 1 Hz-rTMS reflects a protective mechanism of the cortex to avoid a large change in the corticospinal excitability that could destabilize the properties of the neuronal networks (Abbott and Nelson, 2000).”
BackgroundRate and pulse based plasticity governed by local synaptic state variables
Mayr, C. (2010) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2010.00033 · Cited by 51
“Phenomenological models are also often investigated with respect to their computational properties (Abbott and Nelson, 2000; Zou and Destexhe, 2007).”
Phasic Dopamine Modifies Sensory-Driven Output of Striatal Neurons through Synaptic Plasticity
Wieland, S., Schindler, S., Huber, C., Köhr, G., Oswald, M. J., & Kelsch, W. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0127-15.2015 · Cited by 47
“It is, however, possible that different time windows and rules exist for other forms of plasticity (Abbott and Nelson, 2000).”
BackgroundLapenta, O. M., Minati, L., Fregni, F., & Boggio, P. S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00256 · Cited by 51
Homer1a and 1bc levels in the rat somatosensory cortex vary with the time of day and sleep loss
Nelson, S. E., Duricka, D., Campbell, K., Churchill, L., & Krueger, J. M. (2004) · Neuroscience Letters · doi:10.1016/j.neulet.2004.05.089 · Cited by 52
Self-citationAstrocyte regulation of sleep circuits: experimental and modeling perspectives
Fellin, T., Ellenbogen, J. M., Pittà, M. D., Ben‐Jacob, E., & Halassa, M. M. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00065 · Cited by 52
“Through this mechanism, synaptic potentiation could occur without increasing the firing rates of postsynaptic neurons or the steady-state excitability of recurrent networks, and at the same time optimizing STDP-mediated potentiation (Abbott and Nelson, 2000).”
BackgroundInfluentialHayashi, Y. (2013) · Journal of Neurological Disorders · doi:10.4172/2329-6895.1000120 · Cited by 49
“In addition, abnormal microglia has a significant association with neurological disorders [33,34].”
BackgroundEpileptogenesis due to glia-mediated synaptic scaling
Savin, C., Triesch, J., & Meyer‐Hermann, M. (2008) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0387 · Cited by 52
“This type of homeostatic regulation has been demonstrated in various experimental settings (Turrigiano, Leslie, Desai, Rutherford and Nelson 1998, Turrigiano 2007) and its theoretical properties have been studied in computational models (Abbott and Nelson 2000).”
BackgroundSupervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses
Nishitani, Y., Kaneko, Y., & Ueda, M. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2399491 · Cited by 52
“This characteristic is called spike-timing-dependent synaptic plasticity (STDP), which is observed in biological synapses [32]–[34].”
BackgroundLearning, Memory, and the Role of Neural Network Architecture
Hermundstad, A. M., Brown, K., Bassett, D. S., & Carlson, J. M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002063 · Cited by 52
“These competing processes are also maintained in natural neuronal systems and neural circuit models through homeostatic plasticity mechanisms such as synaptic scaling [54,55] and redistribution [56,57], in addition to the rehearsal methods employed here [19–23].”
Dynamic DNA methylation controls glutamate receptor trafficking and synaptic scaling
Sweatt, J. D. (2016) · Journal of Neurochemistry · doi:10.1111/jnc.13564 · Cited by 52
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A., & Poggio, T. (2016) · Current Biology · doi:10.1016/j.cub.2016.10.015 · Cited by 48
How to scale down postsynaptic strength
Tatavarty, V., Sun, Q., & Turrigiano, G. G. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1676-13.2013 · Cited by 50
“Efficient information storage during learning and memory is thought to depend upon the presence of homeostatic mechanisms such as synaptic scaling that normalize synaptic strengths (Abbott and Nelson, 2000; Turrigiano and Nelson, 2004).”
BackgroundSynaptic Properties and Plasticity Mechanisms of Invertebrate Tonic and Phasic Neurons
Aponte-Santiago, N. A., & Littleton, J. T. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.611982 · Cited by 46
“Beyond the visual system, the mammalian hippocampus has been a favorite site for studies of neuronal plasticity, with Hebbian processes like long-term potentiation, long-term depression and spike-timing dependent plasticity representing well-described mechanisms for altering information flow based on input patterns (B…”
MethodPfeifer, K. J., Kromer, J. A., Cook, A. J., Hornbeck, T. S., Lim, E. A., Mortimer, B. J. P., Fogarty, A. S., Han, S. S., Dhall, R., Halpern, C. H., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.624317 · Cited by 50
“To this end, CR employs spike timing-dependent plasticity (STDP), a basic learning mechanism that modifies the strength of synapses according to the relative timing of their corresponding neurons’ presynaptic and postsynaptic spikes or bursts (Markram et al., 1997; Bi and Poo, 1998; Abbott and Nelson, 2000; Caporale a…”
Yan, X., Zhang, Y., Fang, Z., Sun, Y., Liu, P., Sun, J., Jia, X., Sun, S., Guo, Z., & Zhao, Z. (2023) · InfoMat · doi:10.1002/inf2.12429 · Cited by 50
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
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
