Spike-timing-dependent synaptic modification induced by natural spike trains

By Robert Froemke, Y. Dan. 2002

861 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.”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, neutral): ““nearest neighbor takes all” is “nearest neighbor takes more,” in which a discount function is used to weight near spike pairs more heavily than distant spike pairs (Froemke and Dan, 2002).”
A review of learning in biologically plausible spiking neural networks (2019, neutral): “Symmetric triplets STDP in the form of pre-post-pre and post-pre-post, which are a simple example of multiple-spike STDP, are investigated experimentally in (Bi & Wang, 2002; Froemke, Robert C. & Dan, 2002; Froemke, R. C., Tsay, Raad, Long, & Dan, 2006; Wang, H., Gerkin, Nauen, & Bi, 2005).”
Network-timing-dependent plasticity (2015, methodology): “We used the Power-law STDP rule (Morrison et al., 2007) parameterized for cortical (Froemke and Dan, 2002) conductance-based synapses to achieve a mean of ∼1.9 nS under extrinsic input alone as follows: τ+ = 14 ms; τ− = 34 ms; w0 = 4.29 × 10−2 nS; μ = 0.4; λ = 0.1; and α = 4.8 × 10−2.”
Synaptic Mechanisms for Plasticity in Neocortex (2009, background): “Many neocortical excitatory synapses exhibit LTP (Kirkwood & Bear 1994, Castro-Alamancos et al. 1995, Crair & Malenka 1995, Isaac et al. 1997, Markram et al. 1997, Buonomano 1999, Feldman 2000, Sjostrom et al. 2001, Froemke & Dan 2002, Hardingham & Fox 2006).”
Models of Metaplasticity: A Review of Concepts (2015, methodology): “By re-examining the weight traces found in the STDP literature (Bi and Poo, 1998; Sjöström et al., 2001; Froemke and Dan, 2002; Froemke et al., 2006) and reproduced in Figure 1C, it can be seen that the weights actually evolve continuously in vitro.”
Decoupling through synchrony in neuronal circuits with propagation delays. (2008, positive): “First, presynaptic and postsynaptic spikes can be assigned efficacies when suppression is enabled, following the model in Froemke and Dan (2002).”
Reinforcement learning with modulated spike timing dependent synaptic plasticity. (2007, methodology): “Froemke and Dan (2002) fixed the values of these parameters by fitting this model to their data.”
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity (2007, result): “The amplitude of the potentiation is also modulated by ρi (t f i )/ρi (t f i ), in contrast to standard STDP models, but being consistent with the experimentally observed dependence of synaptic modification not only on relative spike timings, but also on inter-spike intervals (Froemke and Dan, 2002)”
Multiple-Color Optical Activation, Silencing, and Desynchronization of Neural Activity, with Single-Spike Temporal Resolution (2007, background): “One prominent example of such a long-standing question is whether synchrony or precise timing of neural activity has an important or causal role in neural computation. Such activity has been observed during, or associated with, various brain functions such as timing-dependent plasticity, global stim”
Simulation of networks of spiking neurons: A review of tools and strategies (2006, neutral): “Furthermore additional variants as suggested in Froemke and Dan (2002) and Gütig et al. (2003) are also implemented.”