Spike-Rate Adaptation in a Computational Model of Human-Shaped Spiral Ganglion Neurons

Objectives:
The purpose of this study is to develop a biophysical model of human spiral ganglion neurons (SGNs) that includes voltage-gated hyperpolarization-activated cation (HCN) channels and low-threshold potassium voltage-gated, delayed-rectifier low-threshold potassium (KLT) channels, providing for a more complete simulation of spike-rate adaptation, a feature of most spiking neurons in which spiking activity is reduced in response to sustained stimulation.
Methods:
Our model incorporates features of spike-rate adaptation reported from in vivo studies, whilst also displaying similar behaviour to existing models of human SGNs, including the dependence of electrically evoked thresholds on the polarity of electrical pulses.
Results:
Hypothesizing that the mode of stimulation-intracellular or extracellular-predicts features of spike-rate adaptation similar to in vivo studies, including the influence of stimulus intensity and pulse-rate, we find that the mode of stimulation alters features of spike-rate adaptation. In particular, the reduction in spiking over time with sustained input was generally greater for extracellular, compared to intracellular, stimulation, when simulating a multi-compartment SGN with human morphological features. In contrast, time-constants of spike-rate adaption reported for in vivo data did not fit our predicted responses, highlighting the need for a more complete physiological understanding of the factors contributing to spike-rate adaptation in electrically stimulated human SGNs.
Conclusion:
Our model extends previous computational models of SGNs with human morphology with ionic channels accounting for features of spike-rate adaptation.
Significance:
The significance of this work resides in the ability to improve the modeling of cochlear implant (CI) stimulation and its effects on neural responses. This will help develop novel, and perhaps personalised, stimulation strategies to reduce variability in CI user outcomes.