Abstract Event cameras, which asynchronously detect local brightness changes with sub-millisecond precision, offer high temporal resolution, wide dynamic range, and efficient data throughput. While these advantages have driven advances in dynamic vision and motion analysis, their application to functional biological imaging remains underexplored. Here we present a comprehensive event-based imaging framework that includes quantitative optical characterization of event cameras, multi-modal in vivo imaging including cortical blood flow and neuronal calcium dynamics, and a novel self-supervised reconstruction algorithm, Implicit Neural Factorization (INF), which converts sparse event streams into continuous activity signals. This framework opens new possibilities for high-resolution, data-efficient functional imaging in biology.
Yoon et al. (Wed,) studied this question.