A unified model of NMDA receptor-dependent bidirectional synaptic plasticity

By Harel Z. Shouval, Mark F. Bear, Leon N. Cooper. Proceedings of the National Academy of Sciences of the United States of America, 2002

665 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a170153d721a49ffe60f8896abaaf4a136d476a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “On the molecular level, the striatal three-factor plasticity depended on NMDA, CaMKII, protein synthesis, and dopamine D1 receptors (Yagishita et al., 2014; Shindou et al., 2018).”
Connectivity reflects coding: a model of voltage-based STDP with homeostasis (2010, result): “In contrast with earlier attempts towards a unified description of synaptic plasticit”
Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex (2012, background): “CaMKII has also been shown to be necessary for structural and long-term plasticity [52,53,4], and may also drive presynaptic changes [54].”
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity (2016, background): “This leads to the possibility that both the triplet and suppression models may arise from a single biophysical mechanism that involves NMDARs [31], but under different conditions for the speed of conformational changes and abundance of second messengers.”
The future of electronics based on memristive systems (2018, background): “…concentration, and in turn the strength of the potentiation, depends on the relative timing of the pre-and post-neuron spikes and this mechanism has been argued as the possible underlying process behind the observed spike-timing-dependent plasticity and rate-dependent plasticity effects 66 .”
State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture (2009, background): “Extra states could, for example, enable us to incorporate the biochemistry of the PSD, leading to a more realistic description of the flow from the basal states into the LTP and LTD states [41].”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, background): “Models of synaptic plasticity typically fall into three classes: phenomenological models, which aim to quantitatively summarize the ever-growing body of experimental data [21–23]; mechanistic models, which aim to explain how these phenomena are produced by underlying biophysical processes [60,73]; a”
Models of Metaplasticity: A Review of Concepts (2015, background): “Calcium signals can act as activity buffers at a timescale τCa2+ (Artola et al., 1990; Shouval et al., 2002; Yeung et al., 2004; Graupner and Brunel, 2012), whereas reward signals or neuromodulation would affect plasticity at a larger timescale τreward (Izhikevich, 2007), comparable to the one…”
Frontiers in Synaptic Neuroscience Synaptic Neuroscience (positive): “model of plasticity that used calcium currents mediated by NMDA receptors as an associative signal (Shouval et al., 2002; Yeung et al., 2004).”
Frontiers in Computational Neuroscience Computational Neuroscience Structural Plasticity Controlled by Calcium Based Correlation Detection (2008, positive): “Taking into account a fi nite short decay time for the postsynaptic depolarization as in Shouval et al. (2002) would lead to a small calcium infl ux also for the postbefore presynaptic timing.”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, neutral): “As an example we present the calcium-dependent plasticity model (CaDP) of (Shouval et al., 2002).”
Spike timing-dependent plasticity: from synapse to perception. (2006, background): “Neuromodulatory inputs may also affect the STDP window by modifying the AP profile through the modulation of the density and properties of axonal and dendritic ion channels, such as transient (IA) and Ca 2 -activated (BK and SK) K channels (95, 112).”
A History of Spike-Timing-Dependent Plasticity (2011, background): “…of well-tuned computer models of cellular learning rules, whether these models are phenomenological or mechanistic in nature, and whether they are formulated within timing or rate-dependent learning rule paradigms (Shouval et al., 2002; Clopath et al., 2010; Rackham et al., 2010; Mihalas, 2011).”
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations (2011, background): “The same problem arises with biophysical models (43, 44) or with more elaborate phenomenological models (45), which have primarily been studied numerically.”