# Light-Field Microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches

arXiv: https://arxiv.org/abs/2110.13142 · HTML: https://mlsanity.com/paper/2110.13142/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Simultaneous Deep Transfer Across Domains and Tasks](https://mlsanity.com/paper/1510.02192/) (2015)
- [Distilling Knowledge from Graph Convolutional Networks](https://mlsanity.com/paper/2003.10477/) (2020)
- [Improved Techniques for Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1809.03625/) (2018)
- [Simultaneous whole-animal 3D-imaging of neuronal activity using light field microscopy](https://mlsanity.com/paper/1401.5333/) (2014)
- [Learning to Reconstruct Confocal Microscopy Stacks from Single Light Field Images](https://mlsanity.com/paper/2003.11004/) (2020)
- [Model-inspired Deep Learning for Light-Field Microscopy with Application to Neuron Localization](https://mlsanity.com/paper/2103.06164/) (2021)

## Abstract (arXiv)

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.