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Computing motion with 3D memristive grid

Computing motion with 3D memristive grid
Computing motion with 3D memristive grid
Computing the relative motion of objects is an important navigation task that we routinely perform by relying on inherently unreliable biological cells in the 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. Here, we introduce a novel memristive thresholding scheme that facilitates the detection of moving edges. In addition, a double-layered 3-D memristive network is employed for modeling the motion computations that take place in both the Outer Plexiform Layer (OPL) and Inner Plexiform Layer (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.
1-11
Lim, Chuan Kai Kenneth
d6a55dff-9b27-4ba9-b5f1-248d26d0543d
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf
Lim, Chuan Kai Kenneth
d6a55dff-9b27-4ba9-b5f1-248d26d0543d
Prodromakis, T.
d58c9c10-9d25-4d22-b155-06c8437acfbf

Lim, Chuan Kai Kenneth and Prodromakis, T. (2013) Computing motion with 3D memristive grid Pre-print, (arXiv:1303.3067), pp. 1-11.

Record type: Article

Abstract

Computing the relative motion of objects is an important navigation task that we routinely perform by relying on inherently unreliable biological cells in the 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. Here, we introduce a novel memristive thresholding scheme that facilitates the detection of moving edges. In addition, a double-layered 3-D memristive network is employed for modeling the motion computations that take place in both the Outer Plexiform Layer (OPL) and Inner Plexiform Layer (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.

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More information

Published date: 13 March 2013
Organisations: Nanoelectronics and Nanotechnology

Identifiers

Local EPrints ID: 351541
URI: http://eprints.soton.ac.uk/id/eprint/351541
PURE UUID: 2a5a1c33-30c0-45ba-ab43-11210210cfc2

Catalogue record

Date deposited: 24 Apr 2013 10:57
Last modified: 18 Jul 2017 04:25

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Contributors

Author: Chuan Kai Kenneth Lim
Author: T. Prodromakis

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