A cascade model of information processing and encoding for retinal prosthesis. How to cite this URL: Pei Zj, Gao Gx, Hao B, Qiao Ql, Ai Hj. How to cite this article: Pei Zj, Gao Gx, Hao B, Qiao Ql, Ai Hj. Keywords: nerve regeneration photoreceptor degeneration retinal prosthesis linear spatiotemporal filter static non-linear rectification spike trains Poisson spike generation synaptic transmission firing rate contrast gain control NSFC grants neural regeneration The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina, which is helpful in developing artificial retina for the retinally blind. Using MATLAB software, spike trains corresponding to stimulus image were numerically computed by four steps: linear spatiotemporal filtering, static nonlinear rectification, radial sampling and then Poisson spike generation. In this study, based on state-of-the-art retinal physiological mechanism, including effective visual information extraction, static nonlinear rectification of biological systems and neurons Poisson coding, a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward, which integrates both anatomic connections and functional computations of retina. However, Most of these focus on stimulus image compression, edge detection and reconstruction, but do not generate spike trains corresponding to visual image. Some retinal models have been presented, ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena. Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue. Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases.
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