Evidence map›Paper›PMID 33121207›Full record

ArticleSensors (Basel, Switzerland)2020

Optical Axons for Electro-Optical Neural Networks.

Mircea Hulea, Zabih Ghassemlooy, Sujan Rajbhandari, Othman Isam Younus, Alexandru Barleanu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Adaptive SNN for Anthropomorphic Finger Control.Sensors (Basel, Switzerland) · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Mircea HuleaFaculty of Automatic Control and Computer Engineering at Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania.ORCID 0000-0001-9888-9183
Zabih GhassemlooyOptical Communications Research Group, Faculty of Engineering and Environment at Northumbria University, Newcastle Upon Tyne NE7 7XA, UK.ORCID 0000-0002-5780-9703
Sujan RajbhandariHuawei Technologies Sweden AB, 412 50 Gothenburg, Sweden.
Othman Isam YounusOptical Communications Research Group, Faculty of Engineering and Environment at Northumbria University, Newcastle Upon Tyne NE7 7XA, UK.ORCID 0000-0003-2015-1175
Alexandru BarleanuFaculty of Automatic Control and Computer Engineering at Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania.
Gheorghe Asachi Technical University of Iași · RONorthumbria University · GBHuawei Technologies (Sweden) · SE

Funding

National Research Grant of the TUIASI GnaC2018 66/01.03.2019 ‎
6 · The paper itself

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.

Indexed as

AxonsNeural Networks, ComputerOptics and PhotonicsSynapsesNeuronsoptical axonsoptical neural networksoptical signal fadingVLC

Identifiers

PMID33121207
PMCPMC7663001
OpenAlexW3096075470

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.