A Non-spiking Model of an Electrically Stimulated Auditory Nerve Fiber

Authors: Rebecca Felsheim1, Mathias Dietz1

1Carl von Ossietzky Universität Oldenburg

Background: Most neuron models produce a spiking output and often represent the stochastic nature of the spike generation process via a stochastic output. Non-spiking models, on the other hand, predict the probability of a spike occurring in response to a stimulus but do not exist for electric stimulation.

Methods: We propose a non-spiking model for an electrically stimulated auditory nerve fiber that not only outputs the total probability of a spike occurring but also the distribution of the spike time in response to each pulse. 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. Afterwards, the model was validated on single cell recordings from cats and guinea pigs.

Results: The model can be fit to all datasets using a single set of parameters and can also predict the validation datasets or at least reproduce the trend in the data.

Conclusions: The non-spiking nature of the aLIFP model makes it fast and deterministic, while still accounting for the stochastic nature of the spike generation process. Therefore, the dependence of the spike probability on changes in either the stimulus or the model parameters can be studied more directly than with stochastically spiking models. The deterministic output and the fast runtime make the model predestined for the combination with other models or inclusion in algorithms.