# Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural Networks

arXiv: https://arxiv.org/abs/2403.04884 · HTML: https://mlsanity.com/paper/2403.04884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 23 of 23)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [Guided Image Generation with Conditional Invertible Neural Networks](https://mlsanity.com/paper/1907.02392/) (2019)
- [Understanding and Mitigating Exploding Inverses in Invertible Neural Networks](https://mlsanity.com/paper/2006.09347/) (2020)
- [Conditional Invertible Neural Networks for Diverse Image-to-Image Translation](https://mlsanity.com/paper/2105.02104/) (2021)
- [Lifting Architectural Constraints of Injective Flows](https://mlsanity.com/paper/2306.01843/) (2023)
- [Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses](https://mlsanity.com/paper/2205.13623/) (2022)
- [Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses](https://mlsanity.com/paper/2306.13104/) (2023)
- [Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision](https://mlsanity.com/paper/2203.05604/) (2022)
- [Unsupervised Domain Transfer with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2303.10191/) (2023)
- [Training Invertible Neural Networks as Autoencoders](https://mlsanity.com/paper/2303.11239/) (2023)
- [A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation](https://mlsanity.com/paper/2302.03570/) (2023)
- [Deep Learning for Visual Neuroprosthesis](https://mlsanity.com/paper/2401.03639/) (2024)

## Built on by (most cited first, 1 of 1)

- [Visual Fixation-Based Retinal Prosthetic Simulation](https://mlsanity.com/paper/2410.11688/) (2024)

## Abstract (arXiv)

Implantable retinal prostheses offer a promising solution to restore partial vision by circumventing damaged photoreceptor cells in the retina and directly stimulating the remaining functional retinal cells. However, the information transmission between the camera and retinal cells is often limited by the low resolution of the electrode array and the lack of specificity for different ganglion cell types, resulting in suboptimal stimulations. In this work, we propose to utilize normalizing flow-based conditional invertible neural networks to optimize retinal implant stimulation in an unsupervised manner. The invertibility of these networks allows us to use them as a surrogate for the computational model of the visual system, while also encoding input camera signals into optimized electrical stimuli on the electrode array. Compared to other methods, such as trivial downsampling, linear models, and feed-forward convolutional neural networks, the flow-based invertible neural network and its conditional extension yield better visual reconstruction qualities w.r.t. various metrics using a physiologically validated simulation tool.