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Light-field microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches

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Title: Light-field microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches
Authors: Foust, A
Song, P
Verinaz Jadan, HI
Howe, C
Dragotti, PL
Item Type: Journal Article
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
Issue Date: 24-Feb-2022
Date of Acceptance: 22-Oct-2021
URI: http://hdl.handle.net/10044/1/94280
DOI: 10.1109/MSP.2021.3123557
ISSN: 1053-5888
Publisher: Institute of Electrical and Electronics Engineers
Journal / Book Title: IEEE: Signal Processing Magazine
Volume: 39
Issue: 2
Copyright Statement: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Royal Academy Of Engineering
Wellcome Trust
Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Council (BBSRC)
Funder's Grant Number: RF1415\14\26
Keywords: Science & Technology
Engineering, Electrical & Electronic
Three-dimensional displays
Computational modeling
Signal processing algorithms
Optical imaging
Deep learning
Light-field microscopy
model-driven and data-driven approaches
Networking & Telecommunications
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
Publication Status: Published
Online Publication Date: 2022-02-24
Appears in Collections:Bioengineering
Faculty of Engineering