Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy.

By Renaud B. Jolivet, Timothy J. Lewis, Wulfram Gerstner. Journal of Neurophysiology, 2004

303 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4cd58547d45ddaf3b3b30c5c716558e37ae05941

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Citations in the corpus

Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems (2014, background): “2 implements a generalized I&F neuron model [61], which has been shown to be extremely versatile and capable of faithfully reproducing the action potentials measured from real cortical neurons [62], [63].”
Stochastic Integrate and Fire Models: a review on mathematical methods and their applications (2011, unknown): “for example [69], [73]).”
Information processing in the primate retina: circuitry and coding. (2007, unknown): “A related model based on leaky integrate-and-fire (LIF) spike generation (Jolivet et al. 2004; Reich et al. 1997, 1998), combined with nonlinear feedback from each spike and a single noise source, reproduced spike trains in primate parasol cells with high fidelity (Figure 5) (Paninski et al. 2004,…”
Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. (2005, background): “For greater convenience and comparison with previously published results (Jolivet et al. 2004), we also used a single performance measure, the coincidence factor (cid:9) , deﬁned in Kistler et al. (1997), ranging from 0 to 1, which is, for small E and M, related to the previous two measures by…”
Global solvability of a networked integrate-and-fire model of McKean–Vlasov type (2012, methodology): “Here Iexti (t) represents the external input current to the neuron, ηi(t) is the noise (a white noise) which is importantly supposed to be independent from neuron to neuron, and the constants λ, β, α and σ are chosen according to experimental data.”
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model (2005, background): “Note that h can assume an arbitrary shape, which allows the model to generate a diverse array of behaviors observed in real neurons, including refractoriness, spike rate adaptation, spike rate facilitation, bursting, and bistability (Jolivet et al., 2004; Paninski et al., 2004).”