In this talk, I will discuss two projects involving recently developed parameter estimation methods for building conductance-based neuronal models from current-clamp data. The first project is focused on circadian rhythms, the biological oscillations that align our physiology and behavior with the 24-hour environmental cycles conferred by the Earth’s rotation. Most of what we know about the electrical activity of circadian clock neurons comes from studies of nocturnal (night-active) rodents, hindering the translation of this knowledge to diurnal (day-active) humans. We use data assimilation and patch-clamp recordings from the diurnal rodent Rhabdomys pumilio to build the first mathematical models of the electrophysiology of circadian neurons in a day-active species. We find that the electrical activity of circadian neurons is similar overall between nocturnal and diurnal rodents but that there are some interesting differences in their responses to inhibition. The second project is focused on a Deep Hybrid Modeling (DeepHM) framework that combines deep learning with mechanistic modeling. Although mechanistic modeling and machine learning methods are both powerful techniques for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings. Model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings and can identify the distributions of mechanistic modeling parameters coherent to the data. We employed DeepHM to identify which ionic conductances are responsible for the altered excitability properties of CA1 pyramidal neurons in mouse models of Alzheimer’s disease.