Spiking Neuron Models

By Wulfram Gerstner, W. Kistler. 2002

1,669 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

Deep learning in neural networks: An overview (2014, methodology): “…(e.g., Amit & Brunel, 1997; Bohte, Kok, & La Poutre, 2002; Brea, Senn, & Pfister, 2013; Brette et al., 2007; Brunel, 2000; Deco & Rolls, 2005; Gerstner & Kistler, 2002; Gerstner & van Hemmen, 1992; Hoerzer, Legenstein, & Maass, 2014; Izhikevich et al., 2003; Kasabov, 2014; Kempter, Gerstner,…”
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008, background): “description of short-term plasticity [44] and on the induction of LTP and LTD [24–26,33–36].”
Self-Delimiting Neural Networks (2012, background): “…of biologically plausible winner-take-all (WITA) neurons with threshold activation functions [50], also found in networks of spiking neurons [16], a given RNN computation might activate just a tiny fraction of all neurons, and hence never even consider the out-going connections of most…”
Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity (2016, methodology): “We focus on a simpler version that only models the modification of synaptic strengths by pre- and postsynaptic spikes [18].”
Which model to use for cortical spiking neurons? (2004, methodology): “Below we review some widely used models of spiking and bursting neurons that can be expressed in the form of ordinary differential equations (ODE) (thus, we exclude the spike response model [5]).”
Dynamical principles in neuroscience (2006, methodology): “For a more detailed description of these models see, for example, Koch 1999 , Gerstner and Kistler 2002 , and Izhikevich 2004 .”
Real-time classification and sensor fusion with a spiking deep belief network (2013, background): “A threshold-linear function can also be used to approximate the expected ﬁring rates of simpliﬁed spiking neurons under constant current stimulation, such as the LIF neuron (Gerstner and Kistler, 2002), which is one of the simplest, yet biologically relatively plausible models for spiking neurons.”
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons (2011, methodology): “We use the same point neuron model as in [1], i.e. stochastic neurons that are formalized in terms of the spike response model [16].”
Evolving spiking neural networks for audiovisual information processing (2010, background): “A traditional theory, suggests that information is transmitted by firing rates (see Gerstner & Kistler, 2002; Mazurek & Shadlen, 2002).”
Excitability in ramped systems: the compost-bomb instability (2011, background): “Similarly, Gerstner & Kistler (2002) distinguish between type I and type II excitabilities that are the same as Hoppensteadt & Izhikevich’s class 1 and 2 excitabilities, respectively.”