Deep Learning for Visual Neuroprosthesis
It addresses the challenge of understanding visual perception for developing neuroprostheses, but is incremental as it reviews existing models without presenting new results.
This chapter discusses the use of deep learning models to construct computational models of the visual pathway, aiming to understand visual encoding and support the implementation of neuroprosthetic devices for enhancing or replacing visual functions.
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.