Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents

David M. Fox, Hua An Tseng, Tomasz G. Smolinski, Horacio G. Rotstein, Farzan Nadim

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Neuronal membrane potential resonance (MPR) is associated with subthreshold and network oscillations. A number of voltage-gated ionic currents can contribute to the generation or amplification of MPR, but how the interaction of these currents with linear currents contributes to MPR is not well understood. We explored this in the pacemaker PD neurons of the crab pyloric network. The PD neuron MPR is sensitive to blockers of H- (IH) and calcium-currents (ICa). We used the impedance profile of the biological PD neuron, measured in voltage clamp, to constrain parameter values of a conductance-based model using a genetic algorithm and obtained many optimal parameter combinations. Unlike most cases of MPR, in these optimal models, the values of resonant- (fres) and phasonant- (fϕ = 0) frequencies were almost identical. Taking advantage of this fact, we linked the peak phase of ionic currents to their amplitude, in order to provide a mechanistic explanation the dependence of MPR on the ICagating variable time constants. Additionally, we found that distinct pairwise correlations between ICaparameters contributed to the maintenance of fresand resonance power (QZ). Measurements of the PD neuron MPR at more hyperpolarized voltages resulted in a reduction of fresbut no change in QZ. Constraining the optimal models using these data unmasked a positive correlation between the maximal conductances of IHand ICa. Thus, although IHis not necessary for MPR in this neuron type, it contributes indirectly by constraining the parameters of ICa.

Original languageEnglish (US)
Article numbere1005565
JournalPLoS computational biology
Volume13
Issue number6
DOIs
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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