PT - JOURNAL ARTICLE AU - Go Ashida AU - Waldo Nogueira TI - Spike-Conducting Integrate-and-Fire Model AID - 10.1523/ENEURO.0112-18.2018 DP - 2018 Jul 01 TA - eneuro PG - ENEURO.0112-18.2018 VI - 5 IP - 4 4099 - http://www.eneuro.org/content/5/4/ENEURO.0112-18.2018.short 4100 - http://www.eneuro.org/content/5/4/ENEURO.0112-18.2018.full SO - eNeuro2018 Jul 01; 5 AB - Modeling is a useful tool for investigating various biophysical characteristics of neurons. Recent simulation studies of propagating action potentials (spike conduction) along axons include the investigation of neuronal activity evoked by electrical stimulation from implantable prosthetic devices. In contrast to point-neuron simulations, where a large variety of models are readily available, Hodgkin–Huxley-type conductance-based models have been almost the only option for simulating axonal spike conduction, as simpler models cannot faithfully replicate the waveforms of propagating spikes. Since the amount of available physiological data, especially in humans, is usually limited, calibration, and justification of the large number of parameters of a complex model is generally difficult. In addition, not all simulation studies of axons require detailed descriptions of nonlinear ionic dynamics. In this study, we construct a simple model of spike generation and conduction based on the exponential integrate-and-fire model, which can simulate the rapid growth of the membrane potential at spike initiation. In terms of the number of parameters and equations, this model is much more compact than conventional models, but can still reliably simulate spike conduction along myelinated and unmyelinated axons that are stimulated intracellularly or extracellularly. Our simulations of auditory nerve fibers with this new model suggest that, because of the difference in intrinsic membrane properties, the axonal spike conduction of high-frequency nerve fibers is faster than that of low-frequency fibers. The simple model developed in this study can serve as a computationally efficient alternative to more complex models for future studies, including simulations of neuroprosthetic devices.