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
Flagged first, then by citations
1 of the 51 references couldn’t be matched to a known paper.
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 801–900 of 999
Frequency-dependent organization of the brain’s functional network through delayed-interactions
Ziaeemehr, A., Zarei, M., Valizadeh, A., & Mirasso, C. R. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.003 · Cited by 31
Modeling Tumor Disease and Sepsis by Networks of Adaptively Coupled Phase Oscillators
Sawicki, J., Berner, R., Löser, T., & Schöll, E. (2022) · Frontiers in Network Physiology · doi:10.3389/fnetp.2021.730385 · Cited by 31
“Although tumor disease and sepsis correspond to different medical conditions, after the initial stages a relatively uniform course develops Weinberg (2014), Singer et al. (2016). The variability of tumor disease results from the initial genetic state of the tumor cells, their subsequent mutations, epithelialmesenchyma…”
BackgroundInfluentialLee, M., Nam, S.-H., Cho, B., Kwon, O., Lee, H. U., Hahm, M. G., Kim, U. J., & Son, H. (2021) · Nano Letters · doi:10.1021/acs.nanolett.1c01885 · Cited by 32
Anti-kindling achieved by stimulation targeting slow synaptic dynamics
Tass, P. A., & Hauptmann, C. (2009) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-2009-0484 · Cited by 31
“Such a form of long-term potentiation (LTP) and long-term depression (LTD) has been observed in many neuronal structures, in particular in the hippocampus (Debanne et al., 1998; Magee and Johnston, 1997; Abbott et al., 2000; Kepecs, 2002; Wittenberg and Wang, 2006; Pfister and Gerstner, 2006).”
Emergence of Optimal Decoding of Population Codes Through STDP
Habenschuss, S., Puhr, H., & Maass, W. (2013) · Neural Computation · doi:10.1162/neco_a_00446 · Cited by 32
“This form of LTD is thus reminiscent of synaptic scaling (Turrigiano, 2010), except that in synaptic scaling, updates scale with the current weight (Abbott & Nelson, 2000).”
BackgroundPlasticity of neuronal excitability: Hebbian rules beyond the synapse.
Campanac, E., & Debanne, D. (2007) · PubMed · Cited by 30
Multistability in Bistable Ferroelectric Materials toward Adaptive Applications
Ghosh, A., Koster, G., & Rijnders, G. (2016) · Advanced Functional Materials · doi:10.1002/adfm.201601353 · Cited by 31
Robustness of STDP to spike timing jitter
Cui, Y., Prokin, I., Mendes, A., Berry, H., & Venance, L. (2018) · Scientific Reports · doi:10.1038/s41598-018-26436-y · Cited by 34
“STDP has been attracting a lot of interest in computational and experimental neurosciences because it relies on spike correlation and has emerged as a candidate mechanism for experience-dependent changes in neural circuits, including map plasticity 8,19–23 .”
Synaptic refinement during development and its effect on slow-wave activity: a computational study
Hoel, E. P., Albantakis, L., Cirelli, C., & Tononi, G. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00812.2015 · Cited by 30
“Therefore, STDP is modeled to affect the synaptic strength w of postsynaptic AMPA and NMDA receptors, which in turn modulates the synaptic peak conductances ( g peak ) of excitatory connections (Abbott and Nelson 2000; Dan and Poo 2004).”
BackgroundToutounji, H., & Pipa, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003512 · Cited by 30
Ionotronic Neuromorphic Devices for Bionic Neural Network Applications
Yu, F., & Zhu, L. Q. (2019) · physica status solidi (RRL) - Rapid Research Letters · doi:10.1002/pssr.201800674 · Cited by 30
On the accuracy and computational cost of spiking neuron implementation
Valadez-Godínez, S., Sossa, H., & Santiago-Montero, R. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.026 · Cited by 30
Atomically Thin Synaptic Devices for Optoelectronic Neuromorphic Vision
Ahmed, T., Jannat, A., Krishnamurthi, V., Aung, T., Mazumder, A., Zavabeti, A., Syed, N., Daeneke, T., Ou, J. Z., Al‐Hourani, A., et al. (2023) · Advanced Materials Technologies · doi:10.1002/admt.202201772 · Cited by 30
Sun, K., Wang, Q., Zhou, L., Wang, J., Chang, J., Guo, R., Tay, B. K., & Yan, X. (2023) · Science China Materials · doi:10.1007/s40843-022-2317-0 · Cited by 30
Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control
Kocatürk, M., Gülçür, H. Ö., & Canbeyli, R. S. (2015) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2015.00008 · Cited by 30
“7, a “homeostatic synaptic plasticity” rule (Turrigiano, 1999; Abbott and Nelson, 2000; Royer and Paré, 2003) is utilized to stabilize the excitability of the MSNs; the excitatory synaptic weights are normalized so that the sum of all weights of excitatory synapses to the j-th MSN is kept at a…”
MethodWaddington, A., Appleby, P. A., de Kamps, M., & Cohen, N. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00088 · Cited by 32
“In this temporally asymmetrical function, apparently causal firing patterns lead to the potentiation of the corresponding synapse whereas apparently anti-causal firing patterns lead to its depression (Abbott and Nelson, 2000; Bi and Poo, 2001; Caporale and Dan, 2008).”
BackgroundA Dynamical Role for Acetylcholine in Synaptic Renormalization
Fink, C. G., Murphy, G. G., Żochowski, M., & Booth, V. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002939 · Cited by 30
“reasonable levels [37,38].”
Contingency Perception and Agency Measure in Visuo-Motor Spiking Neural Networks
Pitti, A., Hiroki, M. O. R. I., KOUZUMA, S., & Kuniyoshi, Y. (2009) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2009.2021506 · Cited by 30
Yasui, T., Fujisawa, S., Tsukamoto, M., Matsuki, N., & Ikegaya, Y. (2005) · The Journal of Physiology · doi:10.1113/jphysiol.2005.086595 · Cited by 30
Vasquez, A., Malavera, A., Doruk, D., Morales-Quezada, L., Carvalho, S., Leite, J., & Fregni, F. (2016) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.12457 · Cited by 30
Serotonergic neuromodulation of synaptic plasticity
Higa, G. S. V., Viana, F. J. C., Francis-Oliveira, J., Cruvinel, E., Franchin, T. S., Marcourakis, T., Ulrich, H., & De Pasquale, R. (2024) · Neuropharmacology · doi:10.1016/j.neuropharm.2024.110036 · Cited by 29
Long-Term Memory Stabilized by Noise-Induced Rehearsal
Wei, Y., & Koulakov, A. A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3929-12.2014 · Cited by 29
Artificial Optoelectronic Synapse Based on Violet Phosphorus Microfiber Arrays
Dong, L., Yuan, S., Wei, G., Zhu, P., Ma, S., Xu, B., & Yang, Y. F. (2023) · Small · doi:10.1002/smll.202306998 · Cited by 29
Savin, C., Dayan, P., & Lengyel, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003489 · Cited by 29
“The shortcomings of assuming additive learning rules can be partially alleviated by introducing additional mechanisms such as synaptic scaling or metaplasticity, that ensure synapses are maintained in the relevant biological range [15].”
BackgroundRanhel, J. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2190421 · Cited by 28
“This characterizes an operational bistable loop capable of memorizing any input pattern occurrence, exemplified here by a pattern initially detected by the PG P .”
BackgroundBarbera, S. L., Vincent, A. F., Vuillaume, D., Querlioz, D., & Alibart, F. (2016) · Scientific Reports · doi:10.1038/srep39216 · Cited by 31
“While this STDP function do not implement the causality between pre and post events, it is reminiscent of biological STDP observed in GABA-ergic neurons in hippocampal culture 32 .”
Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices
Zarudnyi, K., Mehonić, A., Montesi, L., Buckwell, M., Hudziak, S., & Kenyon, A. J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00057 · Cited by 29
Hsu, D., Tang, A., Hsu, M., & Beggs, J. M. (2007) · Physical Review E · doi:10.1103/physreve.76.041909 · Cited by 138
RAI1 Regulates Activity-Dependent Nascent Transcription and Synaptic Scaling
Garay, P. M., Chen, A., Tsukahara, T., Díaz, J. C. R., Kohen, R., Althaus, J. C., Wallner, M. A., Giger, R. J., Jones, K. S., Sutton, M. A., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108002 · Cited by 30
“Synaptic scaling is one mechanism of plasticity, which buffers potentially destabilizing patterns of network activity (Abbott and Nelson, 2000; Miller and MacKay, 1994; Turrigiano, 2008).”
BackgroundStability of neocortical synapses across sleep and wake states during the critical period in rats
Cary, B. A., & Turrigiano, G. G. (2021) · eLife · doi:10.7554/elife.66304 · Cited by 30
Organic electrochemical transistors toward synaptic electronics
Zhou, B., Liu, W., Xu, Y., Jin, C., Yang, J., & Sun, J. (2022) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac67b9 · Cited by 30
“In order to mimic the functionality of neural circuits, the neural functions with biological synapses are required in artificial synapses, such as short-term plasticity (STP 1 ) [52], long-term plasticity (LTP 1 ) [53], spike timing-dependent-plasticity (STDP) [54, 55], etc.”
Brief Subthreshold Events Can Act as Hebbian Signals for Long-Term Plasticity
Fino, É., Deniau, J., & Venance, L. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006557 · Cited by 29
“Consequently, the direction of corticostriatal MSN STDP was reversed compared to that described so far in mammals [4–7].”
BackgroundResultAdaptation controls synchrony and cluster states of coupled threshold-model neurons
Ladenbauer, J., Lehnert, J., Rankoohi, H., Dahms, T., Schöll, E., & Obermayer, K. (2013) · Physical Review E · doi:10.1103/physreve.88.042713 · Cited by 28
Analysis of Synaptic Scaling in Combination with Hebbian Plasticity in Several Simple Networks
Tetzlaff, C., Kolodziejski, C., Timme, M., & Wörgötter, F. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00036 · Cited by 29
“Synaptic scaling is a slow biological process which interacts with faster conventional synaptic plasticity (Abbott and Nelson, 2000; Turrigiano and Nelson, 2000).”
BackgroundMyoga, M. H., Beierlein, M., & Regehr, W. G. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0030-09.2009 · Cited by 30
“…and Sakmann, 1994; Spruston et al., 1995), which is essential to many forms of associative plasticity that require precisely timed presynaptic and postsynaptic activity (Magee and Johnston, 1997; Markram et al., 1997; Abbott and Nelson, 2000; Roberts and Bell, 2002; Sjöström et al., 2008).”
BackgroundInfluentialA Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP
Nere, A., Olcese, U., Balduzzi, D., & Tononi, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036958 · Cited by 27
“STDP is a Hebbian learning paradigm [10–12] which has been shown to be responsible for the strengthening (or weakening) of synapses between neurons in relation to pre- and postsynaptic spike times, both in vitro and in vivo.”
BackgroundVasilaki, E., & Giugliano, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0084626 · Cited by 29
“These specific connectivity and activity configurations are stable when imposing reflecting boundaries, as in the numerical implementation of STDP (52; 51; 53).”
BackgroundMethodMumuni, F., & Mumuni, A. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101188 · Cited by 34
“In biological systems, synaptic plasticity [235] allows the brain to form new connectivity patterns to enable it to encode new information while retaining existing relevant knowledge in memory.”
BackgroundTonelli, P., & Mouret, J.-B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079138 · Cited by 27
“The basis of most of these changes seems to be provided by synaptic plasticity , that is, by the ability of synapses to strengthen or weaken over time [7,17].”
BackgroundMethodInfluentialBird-Inspired Self-Navigating Artificial Synaptic Compass
Kim, Y., Lee, K., Lee, J., Jang, S., Kim, H., Lee, H., Lee, S. W., Wang, G., & Park, C. (2021) · ACS Nano · doi:10.1021/acsnano.1c08005 · Cited by 28
Chen, G., & Gong, P. (2022) · Science Advances · doi:10.1126/sciadv.abl4995 · Cited by 28
“As compared with complex physical systems, a unique feature of neural systems is that there exist diverse synaptic rules operating across multiple time scales such as synaptic scaling and spike timing–dependent plasticity ( 64 ).”
Balaguera, E. H., Muñoz-Díaz, L., Bou, A., Romero, B., Ilyassov, B. R., Guerrero, A., & Bisquert, J. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/accec4 · Cited by 28
“Neuronal connections are highly sensitive to activity-induced patterns (synaptic plasticity) [3], strengthening, or weakening under high- or low-frequency stimulation, respectively.”
BackgroundCreative therapy in health and disease: Inner vision
Khalil, R., & Demarin, V. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14266 · Cited by 31
Hira, R., Ohkubo, F., Masamizu, Y., Ohkura, M., Nakai, J., Okada, T., & Matsuzaki, M. (2014) · Nature Communications · doi:10.1038/ncomms6551 · Cited by 29
A Model of Late Long-Term Potentiation Simulates Aspects of Memory Maintenance
Smolen, P. D. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000445 · Cited by 27
“Empirically, activity-dependent synaptic scaling has been observed in hippocampal slice [6,8] and in cortical cultures and other preparations (reviewed in [7]).”
BackgroundZaitsev, A. V., & Anwyl, R. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00452.2011 · Cited by 28
“…timing are not yet fully understood; however, STDP seems to depend on an interplay between the dynamics of N-methyl-D-aspartate receptor (NMDAR) activation and the timing of APs, which backpropagate through the dendrites of the postsynaptic neuron (Abbott and Nelson 2000; Sjostrom et al. 2008).”
Lv, C., Ma, Q., Han, B., Li, J., Geng, Y., Zhang, X., & Wang, M. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00200 · Cited by 28
“Synaptic plasticity is one of the most important features of the nervous system and serves as the foundation of learning and memory consolidation (Abbott and Nelson, 2000; Citri and Malenka, 2008; Sehgal et al., 2013).”
BackgroundLiu, B., Liu, J., Zhang, J., Mao, W., & Li, S. (2019) · Neuroscience Letters · doi:10.1016/j.neulet.2019.134312 · Cited by 28
Time and rate dependent synaptic learning in neuro-mimicking resistive memories
Ahmed, T., Walia, S., Mayes, E. L. H., Ramanathan, R., Bansal, V., Bhaskaran, M. C., Sriram, S., & Kavehei, O. (2019) · Scientific Reports · doi:10.1038/s41598-019-51700-0 · Cited by 28
Spiking neurons, dopamine, and plasticity: Timing is everything, but concentration also matters
Thivierge, J., Rivest, F., & Monchi, O. (2007) · Synapse · doi:10.1002/syn.20378 · Cited by 28
“First, a model of spike-timing-dependent plasticity (STDP; Abbott and Nelson, 2000; Gerstner and Kistler, 2002; Legenstein et al., 2005; for a review see Dan and Poo, 2004) captures the influence of precise spike timing on synaptic plasticity.”
MethodWhere Are the Switches on This Thing?
Abbott, L. F. (2006) · doi:10.1093/acprof:oso/9780195148220.003.0021 · Cited by 28
Self-citationPresynaptic mechanisms of neuronal plasticity and their role in epilepsy
Meier, J. C., Semtner, M., Winkelmann, A., & Wolfart, J. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00164 · Cited by 27
“Classic synaptic plasticity in its non-homeostatic “Hebbian” form and pathological disturbances need counterbalancing homeostatic scaling mechanisms (Abbott and Nelson, 2000).”
Receptive Field Self-Organization in a Model of the Fine Structure in V1 Cortical Columns
Lücke, J. (2009) · Neural Computation · doi:10.1162/neco.2009.07-07-584 · Cited by 27
“Furthermore, an explicit weight normalization is sometimes used (e.g., Spratling & Johnson, 2002; see Abbott & Nelson, 2000, for a critical discussion).”
MethodEnhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise
Nobukawa, S., & Nishimura, H. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500409 · Cited by 27
“Such synaptic plasticity can lead to synchronous spike propagation with temporal spiking coding information.(11,12) Furthermore, it was reported that STDP may be made efficient by the effect of stochastic resonance (SR) in a noisy environment like the actual brain.”
BackgroundStability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity
Babadi, B., & Abbott, L. F. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004750 · Cited by 29
“However, unless changes in synaptic strength are stabilized appropriately, the level of activity in a neural circuit can grow or shrink in an uncontrolled way [25].”
BackgroundSelf-citationIntracellular calcium stores mediate metaplasticity at hippocampal dendritic spines
Mahajan, G., & Nadkarni, S. (2019) · The Journal of Physiology · doi:10.1113/jp277726 · Cited by 25
“…the plasticity profile of a synapse on an as-needed basis, which may contribute to keeping saturation at bay and maintaining synaptic strengths within a useful dynamic range supporting memory storage and optimal neural network function (Abbott & Nelson, 2000; Abraham, 2008; Keck et al. 2017).”
BackgroundKang, Y., Jang, J., Cha, D., & Lee, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3047963 · Cited by 27
Li, Y., Zhao, D., & Zeng, Y. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.991851 · Cited by 33
“, 2021), lateral inhibition (Blakemore et al., 1970; Abbott and Nelson, 2000), Long-Term Potentiation (LTP) (Malenka, 2003), and Long-Term Depression (LTD) (Ito, 1989) are effective methods.”
Desynchronous learning in a physics-driven learning network
Wycoff, J. F., Dillavou, S., Stern, M., Liu, A. J., & Durian, D. J. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0084631 · Cited by 28
Highly Uniform All‐Vacuum‐Deposited Inorganic Perovskite Artificial Synapses for Reservoir Computing
Chen, L.-W., Wang, W., Ko, S.-H., Chen, C.-Y., Hsu, C.-T., Chiao, F.-C., Chen, T.-W., Wu, K., & Lin, H. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000196 · Cited by 27
Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network
Seok, H., Son, S., Jathar, S. B., Lee, J., & Kim, T. (2023) · Sensors · doi:10.3390/s23063118 · Cited by 28
“In 1949, Hebb et al. suggested a basic mechanism for learning and memory modeling by noting that the synaptic strength increases when the activity in the pre-synaptic neuron repeatedly induces action potentials in the post-synaptic neuron, as shown in Figure 5f [95,98].”
BackgroundWeng, Z., Zheng, H., Wei, L., Jiang, H., Ang, K., & Zhao, Z. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202305386 · Cited by 27
Capone, C., Lupo, C., Muratore, P., & Paolucci, P. S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220743120 · Cited by 27
A De Novo Mutation in TEAD1 Causes Non–X-Linked Aicardi Syndrome
Schrauwen, I., Szelinger, S., Siniard, A. L., Corneveaux, J. J., Kurdoglu, A. A., Richholt, R., Both, M. D. D., Malenica, I., Swaminathan, S., Rangasamy, S., et al. (2015) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.14-16261 · Cited by 27
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
Hanuschkin, A., Diesmann, M., & Morrison, A. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0318-z · Cited by 28
“STDP coupled to hard synaptic weight boundaries has been shown to prevent the destabilization of network activity by Hebbian plasticity (Abbott and Nelson 2000; Turrigiano and Nelson 2004).”
BackgroundNeural field theory of plasticity in the cerebral cortex
Fung, P. K., Haber, A. L., & Robinson, P. A. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.09.030 · Cited by 28
Vocal motor changes beyond the sensitive period for song plasticity
James, L. S., & Sakata, J. T. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00217.2014 · Cited by 29
Evolving recurrent neural networks are super-Turing
Cabessa, J., & Siegelmann, H. T. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033645 · Cited by 27
“This mechanism – referred to as synaptic plasticity – is widely assumed to be intimately related to the storage and encoding of memory traces in the central nervous system [1], and synaptic plasticity provides the basis for most models of learning and memory in neural networks [2].”
BackgroundLoCo: Local Contrastive Representation LearningPreprint
Xiong, Y., Ren, M., & Urtasun, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01342 · Cited by 29
“These classic learning rules can be empirically observed through long-term potentiation (LTP) and long term depression (LTD) events during spike-timing-dependent plasticity (STDP) [1, 8], and various computational learning models have also been proposed [6].”
BackgroundConfavreux, B., Agnes, E. J., Zenke, F., Lillicrap, T., & Vogels, T. P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.24.353409 · Cited by 25
“, spike timing-dependent-plasticity [1–5].”
Predictive sequence learning in the hippocampal formation
Chen, Y., Zhang, H., Cameron, M., & B, T. J. S. A. (2024) · Neuron · doi:10.1016/j.neuron.2024.05.024 · Cited by 40
Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00069 · Cited by 26
“It breaks strong reciprocal connections between neuron pairs (Abbott and Nelson, 2000; Kozloski and Cecchi, 2010).”
BackgroundPalaniyappan, L. K. (2019) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.180038 · Cited by 27
Foxp2 loss of function increases striatal direct pathway inhibition via increased GABA release
van Rhijn, J.-R., Fisher, S. E., Vernes, S. C., & Kasri, N. N. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1746-6 · Cited by 27
Repetitive Transcranial Magnetic Stimulation for Upper Extremity Motor Recovery: Does It Help?
Schambra, H. M. (2018) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-018-0913-8 · Cited by 27
Interlayer Hebbian plasticity induces first-order transition in multiplex networks
Kachhvah, A. D., Dai, X., Boccaletti, S., & Jalan, S. (2020) · New Journal of Physics · doi:10.1088/1367-2630/abcf6b · Cited by 26
Unveiling the role of plasticity rules in reservoir computing
Morales, G. B., Mirasso, C. R., & Soriano, M. C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.05.127 · Cited by 26
“Synaptic plasticity deals directly with the strength of the connection between neurons, which is linked to the amount of neurotransmitter released from the presynaptic neuron and the response generated in the postsynaptic channels [30, 31, 32].”
BackgroundSynaptic Scaling—An Artificial Neural Network Regularization Inspired by Nature
Hofmann, M. R., & Mäder, P. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3050422 · Cited by 29
“(4)], and [52, eq.”
Li, M., Deng, J., Wang, X., Shao, S., Li, X., Gu, W., Wang, H., & Zhao, J. (2021) · Flexible and Printed Electronics · doi:10.1088/2058-8585/abee2d · Cited by 28
Zhao, P., Ji, R., Lao, J., Xu, W. J., Jiang, C., Luo, C., Lin, H., Peng, H., & Duan, C. (2021) · Organic Electronics · doi:10.1016/j.orgel.2021.106390 · Cited by 26
Mburu, M. M., Lu, K., Prine, N. L., Au‐Duong, A., Chiang, W., Gu, X., & Chiu, Y. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202101506 · Cited by 25
On The Biophysical Complexity of Brain Dynamics: An Outlook
Shettigar, N., Yang, C.-L., Tu, K.-C., & Suh, C. S. (2022) · Dynamics · doi:10.3390/dynamics2020006 · Cited by 28
Wang, Z., Guan, Z., Sun, H., Luo, Z. L., Zhao, H., Wang, H., Yin, Y., & Li, X. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c04441 · Cited by 27
Different roles of the medial and lateral hamstrings in unloading the anterior cruciate ligament
Guelich, D. R., Xu, D., Koh, J. L., Nuber, G. W., & Zhang, L. (2016) · The Knee · doi:10.1016/j.knee.2015.07.007 · Cited by 26
“There also exists a quadriceps–hamstrings muscle coactivation response during static knee extension and active knee extension in normal subjects [1,8,15,20].”
BackgroundWhat can neurons do for their brain? Communicate selectivity with bursts
Balduzzi, D., & Tononi, G. (2012) · Theory in Biosciences · doi:10.1007/s12064-012-0165-0 · Cited by 31
“and spike-timing dependent learning rules entail that neurons potentiate or depotentiate their spiking synapses shortly before or after postsynaptic spikes (Hebb 1949; Abbott and Nelson 2000; Caporale and Dan 2008; Pawlak et al. 2010).”
BackgroundInfluentialSleep and Synaptic Homeostasis
Vyazovskiy, V. V., & Faraguna, U. (2014) · Current topics in behavioral neurosciences · doi:10.1007/7854_2014_301 · Cited by 35
“In contrast, Hebbian mechanisms refer to a bidirectional form of plasticity, whereby the weight of synaptic connections between two neurons is increased or decreased depending on precise timing of their respective activities (Abbott and Nelson 2000; Caporale and Dan 2008).”
BackgroundFarajidavar, A., Saeb, S., & Behbehani, K. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.12.021 · Cited by 26
Spike-driven Synaptic Plasticity for Learning Correlated Patterns of Mean Firing Rates
Fusi, S. (2003) · Reviews in the Neurosciences · doi:10.1515/revneuro.2003.14.1-2.73 · Cited by 25
“Since the pre- and the post-synaptic neuron fire asynchronously, the Tacäcs equations that govern the density function p(X,t) of the synaptic internal variable can be easily generalized to incorporate the additional effects of a' and b'.(2) The equations are reported in Appendix B.”
BackgroundVirkar, Y. S., Shew, W. L., Restrepo, J. G., & Ott, E. (2016) · Physical review. E · doi:10.1103/physreve.94.042310 · Cited by 30
“How is this accomplished? Various approaches to this basic question have been considered [2].”
Do Cortical Circuits Need Protecting from Themselves?
Trevelyan, A. J. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2016.06.002 · Cited by 26
Synaptic metaplasticity of protonic/electronic coupled oxide neuromorphic transistor
Ren, Z. Y., Zhu, L. Q., Yu, F., Xiao, H. J., Xiong, W., & Ge, Z. Y. (2019) · Organic Electronics · doi:10.1016/j.orgel.2019.07.028 · Cited by 26
Schloemer, N., Lenz, M., Tegenthoff, M., Dinse, H. R., & Höffken, O. (2020) · Scientific Reports · doi:10.1038/s41598-020-79389-6 · Cited by 25
Dong, S., Liu, H., Wang, Y., Bian, J., & Su, J. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c01489 · Cited by 25
Deng, W., Yan, X., Wang, L., Yu, N., Luo, W., & Mai, L. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.109861 · Cited by 25
Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
Luz, Y., & Shamir, M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004878 · Cited by 24
Pérez, M. Á., Morales, C., Santander, O., García, F., Gómez, I., Peñaloza‐Sancho, V., Fuentealba, P., Dagnino‐Subiabre, A., Moya, P. R., & Fuenzalida, M. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00372 · Cited by 24
“Certainly, chronic exposure to Ket during late adolescent period resulted in long– term impairments of the GABAergic synaptic transmission, which are directly associated with modification of short- and long-lasting changes in synaptic plasticity at excitatory synapses (Abbott and Nelson, 2000; Chevaleyre and Piskorows…”
BackgroundCrossbar Array of Artificial Synapses Based on Ferroelectric Diodes
Seufert, L., Amiri, M. H., Gkoupidenis, P., & Asadi, K. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100558 · Cited by 27
Electrolyte-gated synaptic transistors for brain-inspired computing
Ro, J.-S., An, H., & Park, H. (2022) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/acaca4 · Cited by 25
Dynamics of Hindmarsh–Rose neurons connected via adaptive memristive synapse
Hajian, D. N., Janarthanan, R., Natiq, H., Parastesh, F., Rajagopal, K., & Jafari, S. (2023) · Chinese Journal of Physics · doi:10.1016/j.cjph.2023.11.001 · Cited by 25
Wang, Q., Wang, Y., Wang, Y., Jiang, L., Zhao, J., Song, Z., Bi, J., Zhao, L., Jiang, Z., Schwarzkopf, J., et al. (2024) · InfoMat · doi:10.1002/inf2.12543 · Cited by 25
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