This thesis addresses the critical challenge of real-time spike extraction from high- throughput voltage imaging data, often overwhelming existing analysis pipelines de- spite advancements in genetically encoded voltage indicators and imaging hardware. A novel hybrid processing pipeline is presented, integrating the strengths of state-of- the-art systems, designed for unified offline and online analysis with enhanced mod- ularity and reproducibility. Through architectural optimizations, including streaming- compatible design, algorithmic enhancements like GPU-accelerated filtering, and a robust calibration framework, processing speed and signal fidelity was significantly improved. Our evaluation demonstrates real-time throughput of 500 frames per sec- ond at 680×680 with 8 ms latency and up to 1000 frames per second at 480×480 with 16 ms latency using a Nvidia Tesla V100, notably reducing startup times and improv- ing deployability via a containerized environment. While revealing motion-correction as a persistent bottleneck and the inherent latency-throughput trade-off, this work pro- vides a scalable, accurate, and user-friendly solution that bridges the gap between fast data acquisition and real-time analytical capability, paving the way for next-generation closed-loop neuroscience experiments.