Connectivity reflects coding: a model of voltage-based STDP with homeostasis

By Claudia Clopath, Lars Büsing, Eleni Vasilaki, Wulfram Gerstner. 2010

644 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

A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent “Heterosynaptic” LTD in the Dentate Gyrus In Vivo (2015, neutral): “’s synaptic plasticity model [13] has two voltage thresholds, one for LTD and one for LTP, but the value they used for hippocampal cells were very similar, i.”
Receptive field formation by interacting excitatory and inhibitory synaptic plasticity (2016, methodology): “Synaptic competition can be realized through synaptic learning rules of a simple Hebbian (Linsker, 1986; Miller, 1995; Wimbauer et al., 1997) or Bienenstock-Cooper-Munro (BCM) type (Bienen-stock et al., 1982; Clopath et al., 2010).”
Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules (2010, neutral): “Mechanistic models, which try to account for system level phenomena, like rate-based models, require stabilization mechanisms (Yeung et al., 2004; Yu et al., 2008; Clopath et al., 2010).”
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations (2011, background): “The same problem arises with biophysical models (43, 44) or with more elaborate phenomenological models (45), which have primarily been studied numerically.”
Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location (2012, methodology): “(42) use a similar approach as our approach of fitting a variety of experimental protocols to a simplified model.”
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) (2018, background): “Understanding how the plasticity dynamics in multilayer biological neural networks are organized for eﬃcient data-driven learning is a long-standing question in computational neurosciences (Sussillo and Abbott, 2009; Clopath et al., 2010; Zenke and Ganguli, 2017).”
Dendritic cortical microcircuits approximate the backpropagation algorithm (2018, background): “…plasticity rule depends only on local information available at the synaptic level: postsynaptic ﬁring and dendritic branch voltage, as well as the presynaptic activity, in par with phenomenological models of synaptic plasticity (Sjöström et al., 2001; Clopath et al., 2010; Bono and Clopath, 2017).”
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…”
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).”
Stable Learning in Stochastic Network States (2012, methodology): “…theoretical studies have succeeded in reproducing some aspects of the BCM rule by considering nonlinear interactions be-tween presynaptic and postsynaptic spike patterns in STDP (Senn et al., 2001; Izhikevich and Desai, 2003; Burkitt et al., 2004; Pfister and Gerstner, 2006; Clopath et al., 2010).”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, methodology): “In simulations with metaplastic triplet STDP the amount of long term synaptic depression (LTD) Ai (t) is varied homeostatically as a function of the moving average ni of the postsynaptic firing rate [15,19,30,38] with”
A solution to the learning dilemma for recurrent networks of spiking neurons (2019, result): “In particular they involve postsynaptic depolarization as one of the factors, 157 similarly as the data-based Clopath-rule in [16], see section S6.”
Synaptic consolidation: an approach to long-term learning (2011, background): “However, plasticity does not only depend on pair interactions of spikes (Markram et al. 1997; Sjöström et al. 2001; Froemke et al. 2006; Pfister et al. 2006; Clopath and Gerstner 2010; Clopath et al. 2010).”
Variance and invariance of neuronal long-term representations (2017, neutral): “described spiking network model implementing a biologically plausible voltage-‐dependent spike-‐timing-‐ dependent plasticity rule [88].”