Computing image and motion with 3-d memristive grids

Chuan Kai Kenneth Lim, A. Gelencser, T. Prodromakis*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract / Description of output

In this paper, we first present a biorealistic model for the first part of 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) and Inner Plexiform Layer (IPL) in the vertebrate retina. The non-linear and adaptive response of memristive devices make them excellent building blocks for realizing complex synaptic- like architectures that are common in the human retina. We particularly show how that hexagonal memristive grids can be employed for faithfully emulating the smoothing effect occuring in the OPL to enhance the dynamic range of the system. A memristor-based thresholding scheme is employed for detecting the edges of grayscale images, while evaluating the proposed system’s adaptability to different lighting conditions and fault tolerance capacity. We then extend our work to computing relative motion of objects, which is an important navigation task that vertebrates routinely perform by relying on inherently unreliable biological cells in the retina. Here, a novel memristive thresholding scheme that facilitates the detection of moving edges is introduced. In addition, a double-layered 3-D memristive network is employed for modeling the motion computations that take place in both the OPL and IPL that enables the detection of on-center and off-center transient responses. Applying the transient detection results, it is shown that it is possible to generate an estimation of the speed and direction a moving object.

Original languageEnglish
Title of host publicationHandbook of Memristor Networks
PublisherSpringer International Publishing Switzerland
Number of pages34
ISBN (Electronic)9783319763750
ISBN (Print)9783319763743
Publication statusPublished - 8 Nov 2019


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