1.2. Optimization through digital simulations 1 5 and massive datasets, this class of artificial intelligence algorithms has gained a lot of ground in industry and research. Inspired by their biological counterpart, DNNs consist of basic neuron models that are associated through a network of weighted connections. By ‘learning’ a suitable combination of weights through the observation of thousands or millions of data examples, these highly non-linear networks can be trained to perform complex tasks. The success of DNNs in many visual tasks make them an attractive choice for prosthetic engineers, and a large variety of pre-trained networks are readily available. What these different models have in common is their brain-like ability to condense intricate visual inputs into low-dimensional abstract representations - a convenient property for addressing the scene-simplification problem. Another notable feature of the deep learning framework is its ability to optimize a vast combinatorial space of millions of parameters during training. As explained in later chapters, this property is essential for the computational evaluation of the multitude of design factors that influence the visual quality of prosthetic vision. 1.1.6. Prototyping with simulated prosthetic vision Eventually, both software and hardware features collectively determine the functional outcome of a visual prosthesis. Importantly, the evaluation of different design choices through an iterative process of medical testing can be a challenging, expensive, and timeconsuming undertaking. Hence, the use of simulated prosthetic vision (SPV) paradigms has gained interest among engineers and researchers. In SPV, a visual rendering is created (an image or video frame) that mimics the expected phosphene percept experienced by a prosthesis user. The quality of this simulated phosphene percept can be assessed with the involvement of sighted study participants, offering a non-invasive and cost-effective evaluation method. Since simulations can model beyond the current clinical situation, they are able to assess potential future implant designs. Hereby, they can guide further advancements in the development process. 1.2. Optimization through digital simulations 1.2.1. Research aims While the use of simulation studies has by all means accelerated the cycle of hypothesis forming and experimental testing, there are many aspects that require further exploration. The work presented in the current dissertation embraces the potential of digital simulations in a broad sense - using virtual reality, simulated prosthetic vision and deep neural networks - to address several challenges in the scientific literature: First of all, there is a consensus that SPV research should move towards more immersive simulations, testing complex real-life tasks. The research in the current dissertation aims to narrow the gap between testing visual function and functional vision: basic visual function (e.g., finding edges, shapes) can be tested in relatively controlled setting, while more complex paradigms can help to investigate how functional prosthetic vision can support daily live activities to increase the autonomy of the user. The simulation studies presented inChapters2, 3and6of this dissertation address the functional requirements for mobility and orientation, as an exemplary case of complex daily life activities. Secondly, many of the design features, in particular the scene simplification software, are based on handcrafted strategies and intuitive assumptions. The benefits of proposed
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