A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties

By Anthony N. Burkitt. Biological cybernetics, 2006

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Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “and Cocco, Leibler, and Monasson [52] use an integrateand-fire model [40] to infer how the neurons are interconnected on the basis of time series of spikes in all neurons.”
Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics (2014, methodology): “Experimentally, it has been shown that models of I&F neurons are able to capture rather well the subthreshold dynamics of a real neuron in terms of linear response properties as well as statistical firing properties of a real neuron (Carandini et al., 1996; Rauch et al., 2003; Burkitt, 2006).”
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input (2006, background): “2003) and network stability which are the subjects of a separate review ( Burkitt 2006 ).”