Predictions of the Contribution of HCN Half-Maximal Activation Potential Heterogeneity to Variability in Intrinsic Adaptation of Spiral Ganglion Neurons

Research Article

Abstract

Spiral ganglion neurons (SGNs) exhibit a wide range in their strength of intrinsic adaptation on a timescale of 10s to 100s of milliseconds in response to electrical stimulation from a cochlear implant (CI). The purpose of this study was to determine how much of that variability could be caused by the heterogeneity in half-maximal activation potentials of hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channels, which are known to produce intrinsic adaptation. In this study, a computational membrane model of cat type I SGN was developed based on the Hodgkin-Huxley model plus HCN and low-threshold potassium (KLT) conductances in which the half-maximal activation potential of the HCN channel was varied and the response of the SGN to pulse train and paired-pulse stimulation was simulated. Physiologically plausible variation of HCN half-maximal activation potentials could indeed determine the range of adaptation on the timescale of 10s to 100s of milliseconds and recovery from adaptation seen in the physiological data while maintaining refractoriness within physiological bounds. This computational model demonstrates that HCN channels may play an important role in regulating the degree of adaptation in response to pulse train stimulation and therefore contribute to variable constraints on acoustic information coding by CIs. This finding has broad implications for CI stimulation paradigms in that cell-to-cell variation of HCN channel properties are likely to significantly alter SGN excitability and therefore auditory perception.

Keywords

accommodation cochlear implants (CIs) hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channels refractoriness spike rate adaptation spiral ganglion neuron (SGN) 

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Copyright information

© Association for Research in Otolaryngology 2016

Authors and Affiliations

  1. 1.McMaster Integrative Neuroscience Discovery and StudyMcMaster UniversityHamiltonCanada
  2. 2.Department of Electrical and Computer EngineeringMcMaster UniversityHamiltonCanada

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