Abstract
Sufficient loss of dopamine within the basal ganglia (BG) leads to neuronal activity changes, including altered firing rates and firing patterns, thought to underlie parkinsonian motor symptoms. Yet, within BG neuronal populations, baseline activity and responses to inputs are highly variable, complicating efforts to identify key factors associated with pathological changes. We introduce a novel approach to constructing a computational neuron population model that, when applied to the mouse substantia nigra pars reticulata (SNr), captures the firing heterogeneity observed across slice and in vivo recordings. This model reproduces the diversity of SNr neuron responses to stimulation of GABAergic input terminals, yielding new insights into the mechanisms underlying this variability. Moreover, our modeling pinpoints significant decreases in TRPC3 conductance in SNr dendrites as a key determinant of altered SNr activity in the dopamine depleted state, with important implications for efforts to restore functional SNr activity in this condition.