A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input

By Anthony N. Burkitt. Biological cybernetics, 2006

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Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “For more realistic models of spiking neurons it is often possible to derive or approximate a transfer function that relates a constant input current and neuron parameters (e.g., refractory period, reset voltage, etc.) to an average ﬁring rate (Gerstner and Kistler, 2002; Burkitt, 2006).”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “The input spike train undergoes two rounds of “smoothing,” which means that the membrane potential has a finite rise time rather than experiencing discontinuous jumps in response to incoming spikes [78], [79], [80].”
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: II. Inhomogeneous synaptic input and network properties (2006, methodology): “For a description of the derivation of the this equation and the range over which it is accurate, see Burkitt (2006).”