Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms

TL;DR

This study uses dimensionality reduction on conductance-based models to identify two key physiological mechanisms that explain how neurons maintain stable functions despite high variability in ion channel expression.

Contribution

It reveals two principal dimensions in ion channel conductance space linked to feedback mechanisms, offering new insights into neuronal stability and neuromodulation.

Findings

Identified two principal dimensions explaining most variability in ion channel conductance.

Linked these dimensions to feedback mechanisms regulating neuronal activity.

Developed a model-independent neuromodulation rule for variable neuronal populations.

Abstract

Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which poses questions regarding how specific ion channel subsets reliably shape neuron intrinsic properties. Here, we use detailed conductance-based modeling to explore the origin of stable neuronal function from variable channel composition. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. Those two dimensions correspond to two physiologically relevant sources of variability that can be explained by feedback mechanisms underlying regulation of neuronal activity, providing quantitative insights into how channel composition links to neuronal electrophysiological activity. These insights allowed us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.