The spike timing dependence of plasticity

By D. Feldman. 2012

917 citations recorded by the source corpus.

In spike timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression (LTD). STDP is widely utilized in models of circuit-level plasticity, development, and learning. However, spike timing is just one of several factors (including firing rate, synaptic cooperativity, and depolarization) that govern plasticity induction, and its relative importance varies across synapses and activity regimes. This review summarizes the forms, cellular mechanisms, and prevalence of STDP, and evaluates the evidence that spike timing is an important determinant of plasticity in vivo.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons (2016, background): “STDP is an important and commonly observed mechanism underlying many forms of learning [68].”
Towards a Biologically Plausible Backprop (2016, background): “is particular in that it involves the temporal derivative of the postsynaptic activity, rather than the actual level of postsynaptic activity. Whereas our work focuses on a rate model of neurons, see Feldman (2012) for an overview of synaptic plasticity that goes beyond spike timing and ﬁring rate, ”
Models of Metaplasticity: A Review of Concepts (2015, background): “While it is commonly assumed that NMDA receptors are the primary actors in long-term potentiation, or LTP (Feldman, 2012), the biochemical pathways for long-term depression (LTD) seem to differ in cortex and in hippocampus (Wang et al., 2005; Bender et al., 2006; Nevian and Sakmann, 2006).”
Timing rules for synaptic plasticity matched to behavioral function (2016, neutral): “Throughout the nervous system, synaptic plasticity mechanisms have been described that can capture the correlations between coincident or nearly coincident events (Feldman, 2012).”
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation (2016, neutral): “Whereas our work focuses on a rate model of neurons, see Feldman (2012) for an overview of synaptic plasticity that goes beyond spike timing and ﬁring rate, including synaptic cooperativity (nearby synapses on the same dendritic subtree) and depolarization (due to multiple consecutive pairings or…”
Plasticity of Cortical Excitatory-Inhibitory Balance (2015, background): “Ultimately, the biophysical and biochemical properties of postsynaptic NMDA receptor signaling may underlie the induction of most (but not all) forms of long-term synaptic plasticity (Feldman 2012, Malenka & Nicoll 1999, Urakubo et al. 2008).”
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks (2017, background): “Similar probabilistic approaches have also been used to derive spike timing dependent plasticity (STDP) from information maximizing principles (Bohte and Mozer, 2007; Toyoizumi et al., 2005).”
Synaptic Plasticity Forms and Functions. (2020, background): “Indeed, STDP retains the fundamental autonomous correlation-based property while adding timing requirements that somewhat refine and extend its capabilities (Song & Abbott 2001, Young et al. 2007, Feldman 2012).”
A review of learning in biologically plausible spiking neural networks (2019, methodology): “Spike timing will be a main factor of plasticity in some circumstances; however, it will not be a prominent factor in others (Feldman, 2012).”
The content of hippocampal “replay” (2018, background): “The discovery of symmetric STDP rules in hippocampal area CA3 (Mishra et al., 2016) supports the following simple model of reverse replay.”
A model of grid cell development through spatial exploration and spike time-dependent plasticity. (2014, neutral): “, 2013], there are no E-to-E connections), initially random and weak, are subject to change according to spike-timedependent plasticity (STDP) rules (conventional Hebbian STDP for E-to-I synapses [Lu et al., 2007; Huang et al., 2013; Fino and Venance, 2010; Feldman, 2012]; anti-Hebbian STDP for I-to”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, background): “It is important to remember however that STDP is not a fundamental description of synaptic plasticity, but simply an experimental observation that describes how synapses respond to one particular stimulus of temporally offset spike pairings [3–6].”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, result): “The synaptic connections are subject to various forms of plasticity, and recent experimental results have emphasized the role of STDP, which constantly modifies synaptic strengths (weights) in dependence of the difference between the firing times of the pre- and postsynaptic neurons (see [8,9] for r”