An Algorithm for Modifying Neurotransmitter Release Probability Based on Pre- and Postsynaptic Spike Timing

By Walter Senn, Henry Markram, Misha Tsodyks. 2001

255 citations recorded by the source corpus.

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

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Citations in the corpus

The temporal paradox of Hebbian learning and homeostatic plasticity (2017, methodology): “First, the timescale of Hebbian plasticity, as captured by recent plasticity models fit directly to data from slice experiments [28,35,36,37 ,38], may overestimate the rate of plasticity that actually occurs in vivo.”
State Based Model of Long-Term Potentiation and Synaptic Tagging and Capture (2009, background): “Our model does not take pre-synaptic e ects into account, which can play a rôle in plasticity on shorter time scales than those of e-LTP and `-LTP [38].”
Computational models of long term plasticity and memory (2017, result): “Phenomenological models can describe accurately many other aspects of the experimental observations [Senn et al., 2001, Pfister and Gerstner, 2006, Morrison et al., 2008, Clopath et al., 2010].”
Phenomenological models of synaptic plasticity based on spike timing (2008, result): “Note that earlier efforts to show that STDP maps to the BCM model (Izhikevich and Desai 2003; Senn et al. 2000) demonstrated neither an exact mapping nor a sliding threshold.”
Connectivity reflects coding: a model of voltage-based STDP with homeostasis (2010, methodology): “used in classical rate-based descriptions of developmental learning and, similar to some earlier models of STD”
Suppression without inhibition in visual cortex. (2002, background): “Role of inhibition in the specification of orienta-cortical synapses by the following equation (Senn et al., 2001): tion selectivity of cells in the cat striate cortex.”
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity (2016, methodology): “The original version of the NMDAR-based model [16] includes the dynamics of the probability of presynaptic vesicle release.”
The spike timing dependence of plasticity (2012, background): “In an early study, the firing rate and timing dependence of plasticity was predicted from dynamic activation and calcium-dependent inactivation of NMDA receptors during pre- and postsynaptic spike trains (Senn et al., 2001).”
Spike-timing-dependent synaptic modification induced by natural spike trains (2002, background): “When more quantitative information concerning these processes becomes available, detailed biophysical models”