On the role of L-type Ca 2+ and BK channels in a biophysical model of cartwheel interneurons
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Cartwheel interneurons (CWCs) in the auditory system exhibit a range of activity patterns relevant to auditory function and pathologies. Although experiments have shown how these patterns can vary across individual neurons and can change under pharmacological manipulations, the field has lacked a computational framework in which to explore the contributions of particular currents to these observations and to generate new predictions about the effects of manipulations on CWCs. In this work, we address this deficiency by presenting a conductance-based CWC computational model. This model captures the diversity of CWC activity patterns observed experimentally and suggests parameter changes that may underlie differences across cells. Bifurcation analysis of this model provides an explanation of how distinct dynamic mechanisms contribute to these differences, while direct simulations suggest how cells with different baseline dynamics will respond to variations in certain experimentally-accessible potassium and calcium channel conductances. In addition to the full model that we introduce, we present a reduced model that preserves CWC dynamic regimes. We classify the reduced model variables in terms of distinct dynamic timescales and show that the key transitions in dynamic patterns can be explained based on equilibria of the averaged dynamics of the slowest model variables, in a regime where the faster model variables exhibit oscillations. Overall, this study predicts how changes in parameters will influence CWC behavior, suggests how bifurcations contribute to changes in CWC dynamics, and provides a theoretical foundation that supports our simulation findings.
Author summary
Cartwheel interneurons (CWCs) are the most common class of inhibitory interneurons in an auditory brainstem region involved in sound localization and are believed to be important for auditory processing and pathologies. Distinct patterns of CWC activity have been observed experimentally in a variety of conditions. In this work, we present two novel computational models that simulate the factors contributing to CWC dynamics. By harnessing this framework, we are able to reveal the contributions of key ion currents to modulating CWC activity. Indeed, we find that the factors present in CWC neurons can produce a complicated dynamic landscape, with a wide range of output patterns possible as the relative strengths of these factors are varied. Overall, our models represent useful tools for understanding experimental results and generating new predictions about CWC behavior. In particular, in the more reduced of the two models, we can perform mathematical analysis to make more detailed predictions about the effects of current modulation on whether CWC neurons will exhibit regular spiking or more complex forms of outputs.
