Light-Field Microscopy for the Optical Imaging of Neuronal Activity: When model-based methods meet data-driven approaches

• Published in: IEEE signal processing magazine Vol. 39; no. 2; pp. 58 - 72
• Main Authors: Song, Pingfan, Verinaz-Jadan, Herman, Howe, Carmel L., Foust, Amanda J., Dragotti, Pier Luigi
• Format: Magazine Article
• Language: English
• Published: United States IEEE 01-03-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Computational modeling; Imaging; Machine learning; Microscopy; Neurons; Neuroscience; Optical imaging; Optical microscopy; Signal processing algorithms; Social factors; Three-dimensional displays; Deep learning; Neuroimaging; Light-field microscopy; model-driven and data-driven approaches
• ISSN: 1053-5888; 1558-0792
• DOI: 10.1109/MSP.2021.3123557

Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieving this goal is to be able to observe the dynamics of large populations of neurons over a large area of the brain. Light-field microscopy (LFM), which uses a type of scanless microscope, is a particularly attractive candidate for high-speed 3D imaging. It captures volumetric information in a single snapshot, allowing volumetric imaging at video frame rates. Specific features of imaging neuronal activity using LFM call for the development of novel machine learning approaches that fully exploit the priors embedded in physics and optics models.