Light-Field Microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches. (arXiv:2110.13142v1 [eess.IV])

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.

Source: https://arxiv.org/abs/2110.13142

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