4 40 4. End-to-end optimization of prosthetic vision 4.1. Introduction Globally, over 30 million people suffer from blindness (Stevens et al., 2013). For some forms of blindness, visual prosthetics may provide a promising solution that can restore a rudimentary form of vision (Chen et al., 2020; Fernández, 2018; Lewis et al., 2016; Lozano et al., 2020; Riazi-Esfahani et al., 2014; Roelfsema et al., 2018; Shepherd et al., 2013). These neural interfaces can functionally replace the eye with a camera that is connected to the retina (Weiland et al., 2005; Zrenner et al., 2011), thalamus (Pezaris & Reid, 2007), or visual cortex (Beauchamp & Yoshor, 2020; Chen et al., 2020; Dobelle et al., 1974; Tehovnik & Slocum, 2013). Although in terms of their entry point in the visual system these types of visual prostheses may vary considerably, they share the same basic mechanism of action: through electrical stimulation of small groups of neurons, they evoke a percept of spatially localized flashes of light, called phosphenes (Brindley & Lewin, 1968; Najarpour Foroushani et al., 2018). In this paper, we focus on visual implants that reside in the primary visual cortex (V1), which are reported to have an enormous potential in future treatment of visual impairment (Beauchamp & Yoshor, 2020; Beauchamp et al., 2020; Chen et al., 2020; Lewis et al., 2015). Due to the relatively large surface area, this implantation site allows for stimulation with many electrodes. By selective stimulation and by making use of the roughly retinotopical organization of V1, it is possible to generate a controlled arrangement of phosphenes, in such a way that they may provide a meaningful representation of the visual environment (seeFigure4.1; Chen et al., 2020). Figure 4.1: Schematic illustration of a cortical visual neuro-prosthesis. The visual environment is captured by a camera and sent to a mobile computer. Electrodes in the brain implant are selectively activated to stimulate neurons in the primary visual cortex (V1). Making use of the retinotopic organization of V1, a controlled arrangement of phosphenes can be generated to create a meaningful representation of the visual environment. Compared to normal vision, the percept that can be restored with visual prostheses is very rudimentary and the resolution remains relatively limited, even with relatively high numbers of electrodes. The limited amount of information that can be conveyed allows for only selective visualization of the surroundings. Therefore, an important role in the optimization of prosthetic vision will be fulfilled by image preprocessing techniques. By selective filtering of the visual environment, image preprocessing may help to maximize the usefulness and interpretability of phosphene representations. The choice of filtering is non-trivial and the definition of useful information will strongly depend on the context. Therefore, the implementation and optimization of image preprocessing techniques for
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