Hebbian learning and spiking neurons

By Richard Kempter, Wulfram Gerstner, J. van Hemmen. 1999

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In Neuroscience

Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “Many computational models of such spiking neurons have been proposed and analyzed (e.g., Gerstner and van Hemmen, 1992; Zipser et al., 1993; Stemmler, 1996; Tsodyks et al., 1996; Maex and Orban, 1996; Maass, 1996, 1997; Kistler et al., 1997; Amit and Brunel, 1997; Kempter et al., 1999; Song et al., ”
On the Binding Problem in Artificial Neural Networks (2020, methodology): “This makes them incompatible with gradient-based training, and necessitates a completely different training framework (e.g. Doumas et al., 2008, 2019) typically based on Hebb’s rule (Kempter et al., 1999), or Spike-Timing-Dependent Plasticity (STDP; Caporale and Dan, 2008).”
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008, methodology): “Traditional models of associative memories separate the learning phase from the retrieval phase [23] and the same holds for standard models of STDP [24–26].”
Spike-timing-dependent synaptic modification induced by natural spike trains (2002, methodology): “To predict the effect of a pair of complex spike trains in synaptic modification, a straightforward approach is to combine the contributions of all pre/post spike pairs in the two spike trains”
Models of Metaplasticity: A Review of Concepts (2015, methodology): “As shown in Figures 4A,C, strong competition is observed for both classical STDP and triplet STDP, leading to w2 = 0 for the pathway with weaker correlation c2 < c1 (Kempter et al., 1999; van Rossum et al., 2000; Song and Abbott, 2001).”
Reinforcement learning with modulated spike timing dependent synaptic plasticity. (2007, background): “…et al. 1996; Gütig et al. 2003; Song and Abbott 2001; Song et al. 2000; van Rossum et al. 2000), stabilization of postsynaptic firing rate (Kempter et al. 1999, 2001; Tegnér and Kepecs 2002), enhancement of synchronous firing (Suri and Sejnowski 2002), and coordinate transformations…”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, methodology): “ a LSM. training rules for unsupervised learning of SNNs. A number of approaches for unsupervised learning in spiking neural networks have been developed, based mostly on variants of Hebbian learning [113], such as winner-takes-all learning algorithm in [118]. In [119], a training algorithm that per”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways (2009, methodology): “A combination of stability and divergence gives interesting dynamics, as was shown by Kempter et al. (1999) for the single-neuron case: the first corresponds to partial equilibria (e.g., homeostatic) while the second can imply robust weight specialization through a splitting of their distribution.”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, methodology): “Many of the advances have been made using the linear Poisson neuron model (Gerstner et al. 1996a; Kempter et al. 1999a), in which the probability of spike generation is described by an inhomogeneous Poisson process (van Hemmen 2001).”
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity (2003, methodology): “Using Fokker–Planck mean field theory, the synaptic drifts are described in terms of the correlations between the presynaptic and postsynaptic activity (Kempter et al., 1999, 2001; Kistler and van Hemmen, 2000; Rubin et al., 2001).”
Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations (2016, methodology): “We implemented a hard boundary (Gerstner et al., 1996; Kempter et al., 1999; Song et al., 2000; Drew and Abbott, 2006) in this study to prevent STDP from runaway synaptic dynamics.”
Sparseness constrains the prolongation of memory lifetime via synaptic metaplasticity. (2008, background): “A huge body of theoretical literature has applied and discussed this paradigm in the light of associativememories (Hopfield 1982), self-organized map formation (Linsker 1986), and, more recently, also spiketiming--dependent synaptic plasticity (Gerstner et al. 1996; Kempter et al. 1999; Song et al. ”