  In this paper we present a biorealistic model for the first part of the early
vision processing by incorporating memristive nanodevices. The architecture of
the proposed network is based on the organisation and functioning of the outer
plexiform layer (OPL) in the vertebrate retina. We demonstrate that memristive
devices are indeed a valuable building block for neuromorphic architectures, as
their highly non-linear and adaptive response could be exploited for
establishing ultra-dense networks with similar dynamics to their biological
counterparts. We particularly show that hexagonal memristive grids can be
employed for faithfully emulating the smoothing-effect occurring at the OPL for
enhancing the dynamic range of the system. In addition, we employ a
memristor-based thresholding scheme for detecting the edges of grayscale
images, while the proposed system is also evaluated for its adaptation and
fault tolerance capacity against different light or noise conditions as well as
distinct device yields.
