Benisty Hadas, Song Alexander, Mishne Gal, Charles Adam S
Yale Neuroscience, New Haven, Connecticut, United States.
Max Planck Institute for Intelligent Systems, Stuttgart, Germany.
Neurophotonics. 2022 Oct;9(4):041402. doi: 10.1117/1.NPh.9.4.041402. Epub 2022 Aug 4.
Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
随着光学系统和荧光指示剂的发展,神经科学中的功能光学成像正在迅速发展。为了实现这些海量时空数据集在将神经元活动与行为和刺激相关联以及揭示大脑局部回路方面的潜力,精确的自动化处理变得越来越重要。我们涵盖了神经科学数据功能光学显微镜全数据处理流程中最近的计算进展,并讨论了当前和新出现的挑战。