Optical microscopy methods such as calcium and voltage imaging already enable fast activity readout (30-1000Hz) of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This technological gap not only prevents the execution of novel real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. The fundamental challenge is to reliably extract neural activity from fluorescence imaging frames at speeds compatible with new indicator dynamics and imaging modalities. To meet these challenges and requirements, we propose a framework for Fluorescence Imaging OnLine Analysis (FIOLA). FIOLA exploits computational graphs and accelerated hardware to preprocess fluorescence imaging movies and extract fluorescence traces at speeds in excess of 300Hz on calcium imaging datasets and at speeds over 400Hz on voltage imaging datasets. Besides, we present the first real-time spike extraction algorithm for voltage imaging data. We evaluate FIOLA on both simulated data and real data, demonstrating reliable and scalable performance. Our methods provide the computational substrate required to interface precisely large neuronal populations and machines in real-time, enabling new applications in neuroprosthetics, brain-machine interfaces, and experimental neuroscience. Moreover, this new set of tools is poised to dramatically shorten the experiment-data-theory cycle by providing immediate feedback on the activity of large neuronal populations at experimental time.