In this paper we present a biorealistic model for the first part of the early vision of processing by incorporating memristive nanodevices. The architecture of the proposed network is based on the organization 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 nonlinear and adaptive response could be exploited for establishing ultradense networks with dynamics similar to that of their biological counterparts. We particularly show that hexagonal memristive grids can be employed for faithfully emulating the smoothing effect occurring in the OPL to enhance 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 its distinct device yields.
|Journal||Physical Review E - Statistical, Nonlinear, and Soft Matter Physics|
|Publication status||Published - Apr 26 2012|
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability
- Condensed Matter Physics