Visual information from the surrounding environment is captured by photoreceptors and subsequently converted into electrical neural signals, which then are processed by different excitatory and inhibitory pathways coexisting inside the retinal network and ultimately the output of the retina to higher brain areas by ganglion cells is encoded as spike trains (Seung and Sümbül, 2014). The only exception to this is ganglion cells, whose long axons converge to make up the optic nerve (Siegert et al., 2009). They respond through gradual changes of the membrane potential. Not the same as in the brain, most neurons in the retina are much localized. There are many subclasses within each major cell type (Goetz and Trimarchi, 2012). These visual cells are organized into a layered architecture. Peer review: This paper was double-blinded and stringently reviewed by international expert reviewers.Īs neural entrance of human visual system, the retina contains five different cell types: ganglion, amacrine, horizontal, bipolar cells and photoreceptor.
Plagiarism check: This paper was screened twice using CrossCheck to verify originality before publication. All authors approved the final version of the paper. GXG, BH and HJA was in charge of portions of the modeling and programming. QLQ was responsible for fundraising, directed research, and validated the paper. Available from: Īuthor contributions: ZJP was responsible for modeling, programming and implementation, result analysis and paper writing. A cascade model of information processing and encoding for retinal prosthesis.
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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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