SPIKING NEURON MODELS Single Neurons , Populations , Plasticity

By Wulfram Gerstner. 2002

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “Finally, plasticity rules for spiking neural networks (Maass and Bishop, 2001) aim to demonstrate unique learning mechanisms that emerge from spiking dynamics (Izhikevich, 2007), as well as model biological synaptic plasticity (Gerstner and Kistler, 2002b).”
Spiking Neural P Systems (2006, unknown): “In Figure 1 (a drawing by Ram´on y Cajal, one of the pioneers of neuroscience around 1900, reproduced from [2]), an example of a neural action potential is schematically given altogether with the main parts of a neuron – the cell itself (soma), the axon, the dendrites (a ﬁlamentous bush around the…”
Memristors for Energy‐Efficient New Computing Paradigms (2016, background): “70 ] As the name implies, the integrator is leaky such that the integrated level at a given moment decays with time with no incoming spike in close succession.”
Networks beyond pairwise interactions: structure and dynamics (2020, methodology): “[416] Wulfram Gerstner and Werner M Kistler.”
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016, background): “…models and the wider class of continuous-time dynamical systems: the units of an RNN can be interpreted as the temporal or ensemble average of one or more co-tuned spiking neurons [13], while any nonlinear dynamical system can be approximated by an RNN with a sufficient number of units [14, 15].”
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input (2006, methodology): “A variation of the integrate-and-fire neuron model is the spike response model (Gerstner 1995, 2001; Gerstner and Kistler 2002).”
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection (2009, methodology): “In this research, the so-called α-function is used as the spike response function (Bohte et al., 2002; Gerstner & Kistler, 2002): ε(t) = { t τ e1− t τ when t > 0 0 when t ≤ 0 (2) where τ is the time decay constant that determines the spread shape of the function, as explained in Ghosh-Dastidar and…”
Sequence learning with hidden units in spiking neural networks (2011, background): “With neuron i’s response to past spiking activity ui(t) = xi (t) + ∑ j 6=i wijx ǫ j(t) and the escape rate function ρi(t) = g0 exp (βui(t)) we recovered the defining equations of a simplified stochastic spike response model [7].”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Hence, we will present the LIF model [111] here in detail as it also serves as a building block for understanding other types.”
Dendritic excitability and synaptic plasticity. (2008, unknown): “level of synaptic input; neuronal gain is reduced (243).”
The Future of Memristors: Materials Engineering and Neural Networks (2020, background): “An alternative to dynamic system models is the so-called spike response model (SRM),[258] in which the membrane potential does not need to be simulated by a differential equation, but the membrane potential is used as the sum of the integral kernels.”