Optical Axons for Electro-Optical Neural Networks

Hulea, Mircea, Ghassemlooy, Zabih, Rajbhandari, Sujan, Younus, Othman and Barleanu, Alexandru (2020) Optical Axons for Electro-Optical Neural Networks. Sensors, 20 (21). p. 6119. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s20216119

Abstract

Recently, neuromorphic sensors, which convert analogue signals to spiking frequencies, have ‎been reported for neurorobotics. In bio-inspired systems these sensors are connected to the main neural unit to perform ‎post-processing of the sensor data. The performance of spiking neural networks has been ‎improved using optical synapses, which offer parallel communications between the distanced ‎neural areas but are sensitive to the intensity variations of the optical signal. For systems with ‎several neuromorphic sensors, which are connected optically to the main unit, the use of ‎optical synapses is not an advantage. To address this, in this paper we propose and ‎experimentally verify optical axons with synapses activated optically using digital signals. The ‎synaptic weights are encoded by the energy of the stimuli, which are then optically transmitted ‎independently. We show that the optical intensity fluctuations and link’s misalignment result ‎in delay in activation of the synapses. For the proposed optical axon, we have demonstrated line of ‎sight transmission over a maximum link length of 190 cm with a delay of 8 μs. Furthermore, we ‎show the axon delay as a function of the illuminance using a fitted model for which the root mean square error (RMS) ‎similarity is 0.95.

Item Type: Article
Uncontrolled Keywords: optical neural networks; optical axons; optical signal fading; VLC
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 28 Oct 2020 08:43
Last Modified: 28 Oct 2020 08:47
URI: http://nrl.northumbria.ac.uk/id/eprint/44614

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