  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.
