Deep Learning for Visual Neuroprosthesis

Fuente: arXiv
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Hauptverfasser: Beech, Peter, Jia, Shanshan, Yu, Zhaofei, Liu, Jian K.
Format: Preprint
Veröffentlicht: 2024
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author Beech, Peter
Jia, Shanshan
Yu, Zhaofei
Liu, Jian K.
author_facet Beech, Peter
Jia, Shanshan
Yu, Zhaofei
Liu, Jian K.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_03639
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Deep Learning for Visual Neuroprosthesis
Beech, Peter
Jia, Shanshan
Yu, Zhaofei
Liu, Jian K.
Neurons and Cognition
Artificial Intelligence
Computer Vision and Pattern Recognition
Machine Learning
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.
title Deep Learning for Visual Neuroprosthesis
topic Neurons and Cognition
Artificial Intelligence
Computer Vision and Pattern Recognition
Machine Learning
url https://arxiv.org/abs/2401.03639