An Adaptive Leaky-Integrate and Firing Probability Model of an Electrically Stimulated Auditory Nerve Fiber

Most neural models produce a spiking output and often represent the stochastic nature of the spike generation process via a stochastic output. Nonspiking neural models, on the other hand, predict the probability of a spike occurring in response to a stimulus. We propose a nonspiking model for an electrically stimulated auditory nerve fiber, which not only predicts the total probability of a spike occurring in response to a biphasic pulse but also the distribution of the spike time. Our adaptive leaky-integrate and firing probability (aLIFP) model can account for refractoriness, facilitation, accommodation, and long-term adaptation. All model parameters have been fitted to single cell recordings from electrically stimulated cat auditory nerve fibers. Afterward, the model was validated on recordings from auditory nerve fibers from cats and guinea pigs. The nonspiking nature of the model makes it fast and deterministic while still accounting for the stochastic nature of the spike generation process. Therefore, the relationship between the input to the model or model parameters and the model's output can be observed more directly than with stochastically spiking models.