Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

By Richard Kempter, Wulfram Gerstner, Switzerland J. Leo van Hemmen. 2001

203 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

Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics (2007, background): “The regulatory properties of synaptic plasticity based on spike timing (STDP) have been studied both in recurrent neural networks and at the level of single synapses (see Abbott & Nelson, 2000; Kempter, Gerstner, & van Hemmen, 2001; Rubin, Lee, & Sompolinsky, 2001; Burkitt, Meffin, & Grayden, 2004”
Spike-timing dependent plasticity (2010, background): “For a choice of parameter where the integral over the STDP function is negative (α < 0) and prebefore-post firings lead to potentiation (β > 0), the firing rate of the postsynaptic neuron has a stable fixed point, while the learning rule is sensitive to the temporal correlations between pre- and pos”
Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016, methodology): “STDP was implemented as in previous modeling studies (Song et al., 2000; Kempter et al., 2001; Rubin et al., 2001; Song and Abbott, 2001).”
Relating STDP to BCM (2003, background): “The relationship between the two has been explored by, among others, van Rossum et al. (2000), Senn, Markram, & Tsodyks (2001), Kempter, Gerstner, & van Hemmen (1999, 2001) , Castellani, Quinlan, Cooper, & Shouval (2001), and Abarbanel, Huerta, & Rabinovich (2002), though no attempts have been to c”
SPIKING NEURON MODELS Single Neurons , Populations , Plasticity (2002, background): “On the other hand, competition of synaptic weight changes can also be achieved with purely local learning rules (Oja, 1982; Kistler and van Hemmen, 2000a; Song et al., 2000; Kempter et al., 2001).”
Memory retention and spike-timing-dependent plasticity. (2009, background): “Previous investigations have examined the distribution of the weights and synaptic competition under STDP (Burkitt et al. 2004; Izhikevich and Desai 2003; Song et al. 2000; van Rossum et al. 2000) as well as its stability (Kempter et al. 1999, 2001).”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, unknown): “Functional consequences of STDP can resemble those of rate-based Hebbian models [10], but may also lead to the emergence of temporal coding [11] and ratenormalization [12,13].”
The spike timing dependence of plasticity (2012, unknown): “Some implementations of STDP can also reduce positive feedback instability of synapse strength and network activity that occur commonly with Hebbian learning rules (Song et al., 2000; van Rossum et al., 2000; Kempter et al., 2001; 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…”
Heterosynaptic Plasticity Prevents Runaway Synaptic Dynamics (2013, methodology): “These formal rules are broadly used in computational models of learning and developmental processes (Miller, 1996; Song et al., 2000; Kempter et al., 2001; Rubin et al., 2001; Song and Abbott, 2001; Clopath et al., 2010).”
Dendritic excitability and synaptic plasticity. (2008, unknown): “Despite the instability inherent to Hebbian plasticity (791), STDP may actually impart onto synapses a degree of stability (2, 360, 537, 689, 723, 724, 797) (cf.”