Abstract:

Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases. 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. 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. However, Most of these focus on stimulus image compression,
edge detection and reconstruction, but do not generate spike trains corresponding to visual image. In this study, based on stateof-
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. 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. 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.

Key words: 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