Large scale retinal modeling for the design of new generation retinal prostheses

With the help of modern technology, blindness caused by retinal diseases such as age-related macular degeneration or retinitis pigmentosa is now considered reversible. Scientists from various fields such as Neuroscience, Electrical Engineering, Computer Science, and Bioscience have been collaborating to design and develop retinal prostheses, with the aim of replacing malfunctioning parts of the retina and restoring vision in the blind. Human trials conducted to test retinal prostheses have yielded encouraging results, showing the potential of this approach in vision recovery. However, a retinal prosthesis has several limitations with regard to its hardware and biological functions, and several attempts have been made to overcome these limitations. This thesis focuses on the biological aspects of retinal prostheses: the biological processes occurring inside the retina and the limitations of retinal prostheses corresponding to those processes have been analysed. Based on these analyses, three major findings regarding information processing inside the retina have been presented and these findings have been used to conceptualise retinal prostheses that have the characteristics of asymmetrical and separate pathway stimulations. In the future, when nanotechnology gains more popularity and is completely integrated inside the prosthesis, this concept can be utilized to restore useful visual information such as colour, depth, and contrast to achieve high-quality vision in the blind

Large scale retinal modeling for the design of new generation retinal prostheses

With the help of modern technology, blindness caused by retinal diseases such as age-related macular degeneration or retinitis pigmentosa is now considered reversible. Scientists from various fields such as Neuroscience, Electrical Engineering, Computer Science, and Bioscience have been collaborating to design and develop retinal prostheses, with the aim of replacing malfunctioning parts of the retina and restoring vision in the blind. Human trials conducted to test retinal prostheses have yielded encouraging results, showing the potential of this approach in vision recovery. However, a retinal prosthesis has several limitations with regard to its hardware and biological functions, and several attempts have been made to overcome these limitations. This thesis focuses on the biological aspects of retinal prostheses: the biological processes occurring inside the retina and the limitations of retinal prostheses corresponding to those processes have been analysed. Based on these analyses, three major findings regarding information processing inside the retina have been presented and these findings have been used to conceptualise retinal prostheses that have the characteristics of asymmetrical and separate pathway stimulations. In the future, when nanotechnology gains more popularity and is completely integrated inside the prosthesis, this concept can be utilized to restore useful visual information such as colour, depth, and contrast to achieve high-quality vision in the blind

The Dynamics of Adapting Neurons

How do neurons dynamically encode and treat information? Each neuron communicates with its distinctive language made of long silences intermitted by occasional spikes. The spikes are prompted by the pooled effect of a population of pre-synaptic neurons. To understand the operation made by single neurons is to create a quantitative description of their dynamics. The results presented in this thesis describe the necessary elements for a quantitative description of single neurons. Almost all chapters can be unified under the theme of adaptation. Neuronal adaptation plays an important role in the transduction of a given stimulation into a spike train. The work described here shows how adaptation is brought by every spike in a stereotypical fashion. The spike-triggered adaptation is then measured in three main types of cortical neurons. I analyze in detail how the different adaptation profiles can reproduce the diversity of firing patterns observed in real neurons. I also summarize the most recent results concerning the spike-time prediction in real neurons, resulting in a well-founded single-neuron model. This model is then analyzed to understand how populations can encode time-dependent signals and how time-dependent signals can be decoded from the activity of populations. Finally, two lines of investigation in progress are described, the first expands the study of spike-triggered adaptation on longer time scales and the second extends the quantitative neuron models to models with active dendrites