Abstract
The retina is the first stage of visual neural information coding on the visual system, and several challenges remain on its functioning. Overcoming these challenges would suppose both a step further in the general understanding of the biological neural systems and a potential way to enhance millions of people’s lives that suffer from visual degeneration or impairment. In this work, a data-driven deep learning approach is applied to learn the behavior of mice’s retinal ganglion cells in response to light, as a step towards the development of a system able to mimic a real retina in terms of neural coding of visual stimuli.
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We want to acknowledge Programa de Ayudas a Grupos de Excelencia de la Región de Murcia, from Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia.
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Lozano, A., Garrigós, J., Martínez, J.J., Ferrández, J.M., Fernández, E. (2017). Towards a Deep Learning Model of Retina: Retinal Neural Encoding of Color Flash Patterns. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_46
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