Light-Field Microscopy for Optical Imaging of Neuronal Activity: When Model-Based Methods Meet Data-Driven Approaches.
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Publication Date
2022-03Journal Title
IEEE Signal Process Mag
ISSN
1053-5888
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
39
Issue
2
Pages
58-72
Type
Article
This Version
AM
Physical Medium
Print
Metadata
Show full item recordCitation
Song, P., Jadan, H. V., Howe, C. L., Foust, A. J., & Dragotti, P. L. (2022). Light-Field Microscopy for Optical Imaging of Neuronal Activity: When Model-Based Methods Meet Data-Driven Approaches.. IEEE Signal Process Mag, 39 (2), 58-72. https://doi.org/10.1109/MSP.2021.3123557
Abstract
Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieve 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), a type of scanless microscope, is a particularly attractive candidate for high-speed three-dimensional (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 priors embedded in physics and optics models. Signal processing theory and wave-optics theory could play a key role in filling this gap, and contribute to novel computational methods with enhanced interpretability and generalization by integrating model-driven and data-driven approaches. This paper is devoted to a comprehensive survey to state-of-the-art of computational methods for LFM, with a focus on model-based and data-driven approaches.
Keywords
Deep learning, Light-field microscopy, Neuroimaging, model-driven and data-driven approaches
Identifiers
External DOI: https://doi.org/10.1109/MSP.2021.3123557
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336126
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