A universal workflow for creation, validation, and generalization of detailed neuronal models

Detailed single-neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools focus on a limited set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type. •We introduce a workflow that generates robust and detailed electrical models of neurons•Models made with this workflow match features observed in experimental data•These models show a 5-fold improvement in generalizability compared to existing ones•This workflow provides invaluable tools for in-depth study of a variety of neuronal types Unraveling the complexity of information processing within individual neurons remains a hard task within neuroscience. Our work builds upon the advancements made in computational modeling, introducing a highly versatile and automated workflow for the construction of detailed and robust neuronal models that accurately reproduce experimentally observed electrophysiological behaviors. Utilizing open-source tools, our approach integrates 3D morphological reconstructions of neurons with specific ionic mechanisms. The resulting models offer novel insights into the diversity of neuronal responses observed in the rat somatosensory cortex. Furthermore, these models exhibit a 5-fold enhancement in generalizability compared to canonical models currently in use. Our universal workflow functions as a powerful tool to guide future experiments and enhance our comprehension of neuronal information processing and biophysical mechanisms across various neuronal types. This study presents an automated method for creating detailed models of neurons. The approach successfully captures the complexity of neuronal responses in the brain, specifically the ra