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<p>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.</p>

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Event-based optical imaging and reconstruction of in vivo neuronal and vascular dynamics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Jongmin Yoon,
  • Soi Kim,
  • Seungjae Han,
  • Minho Eom,
  • Eun-Seo Cho,
  • Fatemeh Dehghan Nezhad,
  • Soojung Hong,
  • Eunjee Kim,
  • In-Hyun Park,
  • Euiheon Chung,
  • Kunyoo Shin,
  • Young-Gyu Yoon,
  • Myunghwan Choi

摘要

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.