Deep Learning for Visual Neuroprosthesis

• URL: http://arxiv.org/abs/2401.03639v1
• Date: Mon, 8 Jan 2024 02:53:22 GMT
• Title: Deep Learning for Visual Neuroprosthesis
• Authors: Peter Beech, Shanshan Jia, Zhaofei Yu, Jian K. Liu
• Abstract summary: The visual pathway involves complex networks of cells and regions which contribute to the encoding and processing of visual information. This chapter discusses the importance of visual perception and the challenges associated with understanding how visual information is encoded and represented in the brain.
• Score: 22.59701507351177
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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