Abstract
The retina is responsible for transducing visual information into spikes trains which are then sent via the optical nerve to the visual cortex. This is the first step in the visual pathway responsible for the sense of vision. Our research group is working on the design of a cortical visual neuroprosthesis aimed to restore some functional vision to profoundly visual-impaired people. The goal of developing such a bioinspired retinal encoder is not simply to record a high-resolution image, but to process its visual information and transmit it in a meaningful way to the appropriate area on the visual cortex. Retinal models to be implemented have to match as much as possible the output produced by an actual biological retina. The models involve a big search space defined by a set of parameters that have to be appropriately adjusted. This in itself has several problems which need to be addressed. We propose in this paper an automatic evolutionary multi-objective strategy for selecting those parameters which best approximate the outputs by the synthetic retina model and the biological records. A case study is presented where results of a retina model tuned with our method are compared to biological recordings.
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Crespo-Cano, R., Martínez-Álvarez, A., Díaz-Tahoces, A., Cuenca-Asensi, S., Ferrández, J.M., Fernández, E. (2015). On the Automatic Tuning of a Retina Model by Using a Multi-objective Optimization Genetic Algorithm. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_12
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DOI: https://doi.org/10.1007/978-3-319-18914-7_12
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