A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations

By Julijana Gjorgjieva, Claudia Clopath, Juliette Audet, Jean-Pascal Pfister. 2011

208 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, background): “In practice, such compensatory mechanisms must act on similar or even faster timescales than Hebbian plasticity itself [31 ,32 ,36,37 ,54,55 ,56].”
Emergent Dynamical Properties of the BCM Learning Rule (2017, background): “In addition to the present work contributing to the debate by demonstrating the relevance of fast homeostasis to synaptic stability, it also furthers the discussion as regards the link between STDP and the BCM rule: Zenke et al. [33] found that homeostasis needs to have a faster rate of change for s”
Memory and Information Processing in Neuromorphic Systems (2015, background): “In more elaborate ones, other factors are taken into account, such as the average firing rate of the neurons [95], their analog state (e.”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, methodology): “Tractable simulation of this rule was however made possible by a trick that enabled solution of only a single differential equation per neuron (see Materials and Methods).”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “[53]), we can study empirically the effect of a modification of the frequency of spike-pairing.”
Towards a Biologically Plausible Backprop (2016, background): “ synapse’s distance to the soma). In addition, it would be interesting to study update rules which depend on the statistics of triplets or quadruplets of spikes timings, as in Froemke and Dan (2002); Gjorgjievaa et al. (2011). These effects are not considered here but future work should consider the”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, methodology): “The metaplastic triplet STDP rule we used makes use of an homeostatically modulated rate of LTD and can be mapped to a BCM-like learning rule [30,38].”
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation (2016, background): “ synapse’s distance to the soma). In addition, it would be interesting to study update rules which depend on the statistics of triplets or quadruplets of spikes timings, as in Froemke and Dan (2002); Gjorgjievaa et al. (2011). These effects are not considered here but future work should consider the”
Plasticity of Cortical Excitatory-Inhibitory Balance (2015, methodology): “This has made STDP useful for theoretical studies of synaptic plasticity in neural networks (Clopath et al. 2010, Gjorgjieva et al. 2011, Gütig & Sompolinsky 2006, Pfister & Gerstner 2006, Song et al. 2000, Vogels et al. 2011, Yao et al. 2004), as pre- and postsynaptic spike pairs can be…”