OnlineNeuro: Active online learning for effective exploration of neural simulation parameters

Abstract

Neuromodulation 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.