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

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.

Inertial Algorithm with Dry Fraction and Convolutional Sparse Coding for 3D Localization with Light Field Microscopy

“…Light field microscopy (LFM) is considered a highly effective, scan-free imaging technique for observing the location and morphology of large numbers of neurons. It enables real-time observation of the communication dynamics of 3D neuronal networks in the brain (Cong et al 2017;Song et al 2022). It drastically reduces the time required for 3D reconstruction compared to conventional scanning imaging techniques and optimized solutions like multiplanar and confocal microscopy (Bai et al 2022;Song et al 2020;Pégard et al 2016;Lu et al 2023).…”

“…They provided the PSF (point spread function) based on fluctuating optics theory to accurately simulate light field microscopy. However, iterative R-L deconvolution methods suffer from high computational complexity and may also produce reconstruction artifacts (Daube-Witherspoon and Muehllehner 1986;Nöbauer et al 2017;Lu et al 2019;Song et al 2022).…”

Toward Immersive Communications in 6G

“…Motivated by the potentials of immersive communications, scientists and engineers over the world have been working on the development of related technologies, products, and platforms. Significant progress has been made in recent years, including but not limited to advancements in sensor systems and data capture techniques [15], [16], data processing and computing frameworks [17]- [19], and rendering and display devices [20]- [22]. Some component development of immersive communications is progressing faster than others, leading to the establishment of testbeds, prototypes, or even commercial products.…”