Nieves Avendano, DiegoDiegoNieves AvendanoCallaert, ArneArneCallaertSchnepel, PhilippPhilippSchnepelMihajlovic, VojkanVojkanMihajlovicOngenae, FemkeFemkeOngenaeVan Hoecke, SofieSofieVan Hoecke2026-07-162026-07-1620262352-7110https://imec-publications.be/handle/20.500.12860/59890Neuromodulation studies require efficient exploration of high-dimensional stimulation spaces, where heuristic tuning is often slow and suboptimal. We present OnlineNeuro, an open-source Python framework that combines active learning with neural simulators (AxonSim, Cajal, and AxonML). The package offers a unified interface for experiment setup, model training, adaptive sampling, and reporting. By prioritizing informative queries, OnlineNeuro improves sample efficiency for parameter exploration and meta-model construction. We demonstrate the framework on neural simulation use cases and benchmark tasks.engJournal article10.1016/j.softx.2026.102648WOS:001745525000001BAYESIAN OPTIMIZATION