Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity

By Jean-Pascal Pfister, Wulfram Gerstner. 2006

669 citations recorded by the source corpus.

Classical experiments on spike timing-dependent plasticity (STDP) use a protocol based on pairs of presynaptic and postsynaptic spikes repeated at a given frequency to induce synaptic potentiation or depression. Therefore, standard STDP models have expressed the weight change as a function of pairs of presynaptic and postsynaptic spike. Unfortunately, those paired-based STDP models cannot account for the dependence on the repetition frequency of the pairs of spike. Moreover, those STDP models cannot reproduce recent triplet and quadruplet experiments. Here, we examine a triplet rule (i.e., a rule which considers sets of three spikes, i.e., two pre and one post or one pre and two post) and compare it to classical pair-based STDP learning rules. With such a triplet rule, it is possible to fit experimental data from visual cortical slices as well as from hippocampal cultures. Moreover, when assuming stochastic spike trains, the triplet learning rule can be mapped to a Bienenstock–Cooper–Munro learning rule.

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.”
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008, positive): “Our model of LTP/LTD induction does not only account for voltage and frequency dependence of LTP/LTD induction, but also for spike timing dependence. In fact, for a stimulation paradigm where postsynaptic spikes are induced by short current”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “STDP is also frequency dependent, which can be explained by rules that depend on triplets rather than pairs of spikes (Pﬁster and Gerstner, 2006).”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, methodology): “When we estimated t w we explicitly ignored correlations and required that LTD and LTP cancel at a firing rate k .”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, methodology): “Thetracesare givenbythefollowing: In the original paper, parameters of the triplet rule have been optimized for weight induction (Pfister and Gerstner, 2006).”
Models of Metaplasticity: A Review of Concepts (2015, methodology): “…STDP models based on the curve, in order to incorporate the observed effects for triplets of spikes, spike bursts, clamping the postsynaptic membrane potential and so on (Pfister and Gerstner, 2006; Clopath et al., 2010; El Boustani et al., 2012; Graupner and Brunel, 2012; Yger and Harris, 2013).”
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations (2011, methodology): “To get depression at low postsynaptic firing rate and potentiation for higher firing rate, pairs of spikes must have an overall depressive effect ðW2 < 0Þ and triplets of spikes must induce potentiation ðW3 > 0Þ, as is the case for the minimal triplet STDP model considered here (15).”
Receptive field formation by interacting excitatory and inhibitory synaptic plasticity (2016, methodology): “We have observed similar dynamics when the excitatory learning rule was replaced by a rate-based triplet rule that includes long-term depression (Pfister & Gerstner, 2006; Clopath et al., 2010), as long as the threshold rate between potentiation and depression in the triplet rule was lower than the…”
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks (2013, background): “To derive plasticity rules that solve this learning task, we chose the neural dynamics and minimized with respect to synaptic weights a distance measure between stimulus-evoked and spontaneous activity (see Materials and Methods; Fig.”
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020, methodology): “Then, we explored the behavior of our rule when the pre and postsynaptic neuron fire independently according to Poisson statistics [74] (Fig.”
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity (2016, methodology): “Pfister & Gerstner [15] also provided a version of the triplet model based only on nearest neighboring spikes, but the qualitative behavior of both versions is similar.”