# Deep Learning for Visual Neuroprosthesis

arXiv: https://arxiv.org/abs/2401.03639 · HTML: https://mlsanity.com/paper/2401.03639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 75 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI](https://mlsanity.com/paper/1907.02431/) (2019)

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

- [Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2403.04884/) (2024)

## Abstract (arXiv)

The visual pathway involves complex networks of cells and regions which contribute to the encoding and processing of visual information. While some aspects of visual perception are understood, there are still many unanswered questions regarding the exact mechanisms of visual encoding and the organization of visual information along the pathway. This chapter discusses the importance of visual perception and the challenges associated with understanding how visual information is encoded and represented in the brain. Furthermore, this chapter introduces the concept of neuroprostheses: devices designed to enhance or replace bodily functions, and highlights the importance of constructing computational models of the visual pathway in the implementation of such devices. A number of such models, employing the use of deep learning models, are outlined, and their value to understanding visual coding and natural vision is discussed.