Nano-scale reservoir computing

Oliver Obst; Adrian Trinchi; Simon G. Hardin; Matthew Chadwick; Ivan Cole; Tim H. Muster; Nigel Hoschke; Diet Ostry; Don Price; Khoa N. Pham; Tim Wark

arXiv · 1309.1521

Nano-scale reservoir computing

Oliver Obst·Adrian Trinchi·Simon G. Hardin·Matthew Chadwick·Ivan Cole·Tim H. Muster·Nigel Hoschke·Diet Ostry·Don Price·Khoa N. Pham·Tim Wark

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Abstract

This work describes preliminary steps towards nano-scale reservoir computing using quantum dots. Our research has focused on the development of an accumulator-based sensing system that reacts to changes in the environment, as well as the development of a software simulation. The investigated systems generate nonlinear responses to inputs that make them suitable for a physical implementation of a neural network. This development will enable miniaturisation of the neurons to the molecular level, leading to a range of applications including monitoring of changes in materials or structures. The system is based around the optical properties of quantum dots. The paper will report on experimental work on systems using Cadmium Selenide (CdSe) quantum dots and on the various methods to render the systems sensitive to pH, redox potential or specific ion concentration. Once the quantum dot-based systems are rendered sensitive to these triggers they can provide a distributed array that can monitor and transmit information on changes within the material.

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Oliver Obst, Adrian Trinchi, Simon G. Hardin, Matthew Chadwick, Ivan Cole, Tim H. Muster, Nigel Hoschke, Diet Ostry, Don Price, Khoa N. Pham, Tim Wark. 2013-09-06. Nano-scale reservoir computing. https://arxiv.org/abs/1309.1521

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