A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices

TL;DR

This paper introduces a biorealistic model of the outer plexiform layer of the retina using memristive devices, demonstrating their potential for dense, adaptive neuromorphic vision systems with enhanced dynamic range and fault tolerance.

Contribution

It presents a novel memristor-based architecture mimicking the retina's early visual processing, including smoothing and edge detection functionalities.

Findings

Memristive grids effectively emulate retinal smoothing effects.

The system successfully detects edges in grayscale images.

The architecture shows robustness to noise and device faults.

Abstract

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.