{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T11:12:23Z","timestamp":1760181143704,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Grant of the TUIASI","award":["GnaC2018 66\/01.03.2019 \u200e"],"award-info":[{"award-number":["GnaC2018 66\/01.03.2019 \u200e"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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 \u03bcs. 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.<\/jats:p>","DOI":"10.3390\/s20216119","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T21:21:00Z","timestamp":1604006460000},"page":"6119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Optical Axons for Electro-Optical Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9888-9183","authenticated-orcid":false,"given":"Mircea","family":"Hulea","sequence":"first","affiliation":[{"name":"Faculty of Automatic Control and Computer Engineering at Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5780-9703","authenticated-orcid":false,"given":"Zabih","family":"Ghassemlooy","sequence":"additional","affiliation":[{"name":"Optical Communications Research Group, Faculty of Engineering and Environment at Northumbria University, Newcastle upon Tyne NE7 7XA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujan","family":"Rajbhandari","sequence":"additional","affiliation":[{"name":"Huawei Technologies Sweden AB, 412 50 Gothenburg, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2015-1175","authenticated-orcid":false,"given":"Othman Isam","family":"Younus","sequence":"additional","affiliation":[{"name":"Optical Communications Research Group, Faculty of Engineering and Environment at Northumbria University, Newcastle upon Tyne NE7 7XA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru","family":"Barleanu","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computer Engineering at Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","article-title":"Networks of spiking neurons: The third generation of neural network models","volume":"10","author":"Maass","year":"1997","journal-title":"Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kasabov, N. (2019). Deep learning of multisensory streaming data for predictive modelling with applications in finance, ecology, transport and environment. Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-662-57715-8_19"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"774","DOI":"10.3389\/fnins.2018.00774","article-title":"Deep learning with spiking neurons: Opportunities and challenges","volume":"12","author":"Pfeiffer","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5352","DOI":"10.3390\/s8095352","article-title":"Neuromorphic VLSI models of selective attention: From single chip vision sensors to multi-chip systems","volume":"8","author":"Indiveri","year":"2008","journal-title":"Sensors"},{"key":"ref_5","unstructured":"Vogelstein, R.J., Mallik, U., Culurciello, E., Cauwenberghs, G., and Etienne-Cummings, R. (2005). Saliency-driven image acuity modulation on a reconfigurable array of spiking silicon neurons. Adv. Neural Inf. Process. Syst., 1457\u20131464."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wunderlich, T., Kungl, A., M\u00fcller, E., Hartel, A., Stradmann, Y., Aamir, S.A., Gr\u00fcbl, A., Heimbrecht, A., Schreiber, K., and St\u00f6ckel, D. (2019). Demonstrating advantages of neuromorphic computation: A pilot study. Front. Neurosci.","DOI":"10.3389\/fnins.2019.00260"},{"key":"ref_7","first-page":"3227","article-title":"Supervised learning based on temporal coding in spiking neural networks","volume":"29","author":"Mostafa","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.conb.2010.03.007","article-title":"Neuromorphic sensory systems","volume":"20","author":"Liu","year":"2010","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MCSE.2017.33","article-title":"A neuromorph\u2019s prospectus","volume":"19","author":"Boahen","year":"2017","journal-title":"Comput. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11441","DOI":"10.1073\/pnas.1604850113","article-title":"From the cover: Convolutional networks for fast, energy-efficient neuromorphic computing","volume":"113","author":"Esser","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1523\/JNEUROSCI.1425-06.2006","article-title":"Triplets of spikes in a model of spike timing-dependent plasticity","volume":"26","author":"Pfister","year":"2006","journal-title":"J. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hulea, M., Ghassemlooy, Z., and Rajbhandari, S. (2018, January 18\u201320). Spiking neural network with visible light communication. Proceedings of the 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), Budapest, Hungary.","DOI":"10.1109\/CSNDSP.2018.8471811"},{"key":"ref_13","first-page":"7430","article-title":"Neuromorphic photonic networks using silicon photonic weight banks","volume":"7","author":"Tait","year":"2017","journal-title":"Sci. Rep. Nat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1364\/OPN.29.1.000034","article-title":"Principles of neuromorphic photonics","volume":"29","author":"Nahmias","year":"2018","journal-title":"Opt. Photonics News"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Peng, H.-T., Nahmias, M., de Lima, T.F., Tait, A., Shastri, B.J., and Prucnal, P. (2018). Neuromorphic photonic integrated circuits. IEEE J. Sel. Top. Quantum Electron., 24.","DOI":"10.1109\/JSTQE.2018.2840448"},{"key":"ref_16","first-page":"4","article-title":"Photonic spike processing: Ultrafast laser neurons and an integrated photonic network","volume":"28","author":"Shastri","year":"2014","journal-title":"IEEE Photonics Soc. Newsl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Uysal, M., Capsoni, C., Ghassemlooy, Z., Boucouvalas, A.C., and Udvary, E.G. (2016). Optical Wireless Communications\u2014An Emerging Technology, Springer.","DOI":"10.1007\/978-3-319-30201-0"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1364\/AO.24.001469","article-title":"Optical implementation of the Hopfield model","volume":"24","author":"Farhat","year":"1985","journal-title":"Appl. Opt."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3852","DOI":"10.1364\/AO.28.003852","article-title":"Adaptive neural network in a hybrid optical\/electronic architecture using lateral inhibition","volume":"28","author":"Noll","year":"1989","journal-title":"Appl. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/0030-3992(95)00070-4","article-title":"Overview of hybrid optical neural networks","volume":"28","author":"Jutamulia","year":"1996","journal-title":"Opt. Laser Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9620","DOI":"10.1364\/OE.27.009620","article-title":"An all-optical neuron with sigmoid activation function","volume":"27","author":"Tsakyridis","year":"2019","journal-title":"Opt. Express"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14009","DOI":"10.1364\/OE.27.014009","article-title":"Design of optical neural networks with component imprecisions","volume":"27","author":"Fang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1364\/OPTICA.5.000864","article-title":"Training of photonic neural networks through in situ backpropagation","volume":"5","author":"Hughes","year":"2018","journal-title":"Optica"},{"key":"ref_24","first-page":"12980","article-title":"Toward fast neural computing using all-photonic phase change spiking neurons","volume":"8","author":"Chakraborty","year":"2018","journal-title":"Sci. Rep. Nat."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"064043","DOI":"10.1103\/PhysRevApplied.11.064043","article-title":"A silicon photonic modulator neuron","volume":"11","author":"Tait","year":"2019","journal-title":"Phys. Rev. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1038\/ncomms4541","article-title":"Experimental demonstration of reservoir computing on a silicon photonics chip","volume":"5","author":"Fiers","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tait, A., Zhou, E., Wu, A., Nahmias, M., de Lima, T.F., Shastri, B., and Prucnal, P. (2016, January 11\u201313). Demonstration of a silicon photonic neural network. Proceedings of the 2016 IEEE Photonics Society Summer Topical Meeting Series (SUM), Newport Beach, CA, USA.","DOI":"10.1109\/PHOSST.2016.7548726"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1038\/s41586-019-1157-8","article-title":"All-optical spiking neurosynaptic networks with self-learning capabilities","volume":"569","author":"Feldmann","year":"2019","journal-title":"Nature"},{"key":"ref_29","first-page":"190082","article-title":"Photonic synapses for ultrahigh-speed neuromorphic computing","volume":"13","author":"Zhuge","year":"2019","journal-title":"Rapid Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5181","DOI":"10.1364\/OE.27.005181","article-title":"Neuromorphic photonics with electro-absorption modulators","volume":"27","author":"George","year":"2019","journal-title":"Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6463","DOI":"10.1021\/acsami.8b01036","article-title":"Memristive synapses with photoelectric plasticity realized in zno1\u2013x\/aloy heterojunction","volume":"10","author":"Hu","year":"2018","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qin, S., Liu, Y., Wang, X., Xu, Y., Shi, Y., Zhang, R., and Wang, F. (2017, January 14\u201319). Photonic synaptic device capable of optical memory and logic operations. Proceedings of the 2017 Conference on Lasers and Electro-Optics (CLEO), San Jose, CA, USA.","DOI":"10.1364\/CLEO_SI.2017.SW4K.7"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1700951","DOI":"10.1002\/adma.201700951","article-title":"Brain-inspired photonic neuromorphic devices using photodynamic amorphous oxide semiconductors and their persistent photoconductivity","volume":"29","author":"Lee","year":"2017","journal-title":"Adv. Mater."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1002\/adma.200902170","article-title":"Two-terminal carbon nanotube programmable devices for adaptive architectures","volume":"22","author":"Agnus","year":"2010","journal-title":"Adv. Mater."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"eaat7387","DOI":"10.1126\/sciadv.aat7387","article-title":"Stretchable organic optoelectronic sensorimotor synapse","volume":"4","author":"Lee","year":"2018","journal-title":"Sci. Adv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5900214","DOI":"10.1109\/JSTQE.2016.2573583","article-title":"Microring weight banks","volume":"22","author":"Tait","year":"2016","journal-title":"IEEE J. Sel. Topics Quantum Electron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4029","DOI":"10.1109\/JLT.2014.2345652","article-title":"Broadcast and weight: An integrated network for scalable photonic spike processing","volume":"32","author":"Tait","year":"2014","journal-title":"J. Lightw. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"071110","DOI":"10.1063\/1.2337162","article-title":"Tunable silicon microring resonator with wide free spectral range","volume":"89","author":"Nawrocka","year":"2006","journal-title":"Appl. Phys. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12138","DOI":"10.1364\/OE.391473","article-title":"Experimental realization of arbitrary activation functions for optical neural networks","volume":"28","author":"Williamson","year":"2020","journal-title":"Opt. Express"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3389\/fnins.2016.00115","article-title":"A review of current neuromorphic approaches for vision, auditory, and olfactory sensors","volume":"10","author":"Vanarse","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., and Rassau, A. (2017). An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems. Sensors, 17.","DOI":"10.3390\/s17112591"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., and Rassau, A. (2019). Real-time classification of multivariate olfaction data using spiking neural networks. Sensors, 19.","DOI":"10.3390\/s19081841"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Vanarse, A., Osseiran, A., Rassau, A., and van der Made, P. (2019). A hardware-deployable neuromorphic solution for encoding and classification of electronic nose data. Sensors, 19.","DOI":"10.3390\/s19224831"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s41467-019-13827-6","article-title":"An artificial spiking afferent nerve based on Mott memristors for neurorobotics","volume":"11","author":"Zhang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hulea, M., Uleru, G., Burlacu, A., and Caruntu, C. (2020, January 21\u201323). Bioinspired SNN for robotic joint control. Proceedings of the 2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Cluj-Napoca, Romania.","DOI":"10.1109\/AQTR49680.2020.9129887"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1950012","DOI":"10.1142\/S0219843619500129","article-title":"Intelligent motion planning and control for robotic joints using bio-inspired spiking neural networks","volume":"16","author":"Hulea","year":"2019","journal-title":"Int. J. Hum. Robot."},{"key":"ref_47","unstructured":"Hulea, M. (2018). Electronic Circuit for Modeling an Artificial Neuron. (RO-126249), Romanian Patent, priority date: 29.04.2011, granted."},{"key":"ref_48","unstructured":"Hulea, M. (2011, January 14\u201316). The mathematical model of a biologically inspired electronic neuron for ease the design of spiking neural networks topology. Proceedings of the 15th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania."},{"key":"ref_49","unstructured":"Schuman, C.D., Potok, E.T., Patton, R.M., Birdwell, J.D., Dean, E.M., Rose, S.G., and Plank, S.J. (2017). A survey of neuromorphic computing and neural networks in hardware. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1038\/35090500","article-title":"Efficiency and ambiguity in an adaptive neural code","volume":"412","author":"Fairhall","year":"2001","journal-title":"Nature"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1038\/nn.3431","article-title":"Temporal whitening by power-law adaptation in neocortical neurons","volume":"16","author":"Pozzorini","year":"2013","journal-title":"Nat. Neurosci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1038\/nn1067","article-title":"Activity-dependent activation of presynaptic protein kinase C mediates post-tetanic potentiation","volume":"6","author":"Brager","year":"2003","journal-title":"Nat. Neurosci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1126\/science.1128134","article-title":"Learning induces long-term potentiation in the hippocampus","volume":"313","author":"Whitlock","year":"2016","journal-title":"Science"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4849","DOI":"10.1364\/OL.44.004849","article-title":"Filter-less WDM for visible light communications using colored pulse amplitude modulation","volume":"44","author":"Burton","year":"2019","journal-title":"Opt. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3389\/fnins.2018.00987","article-title":"Sparse computation in adaptive spiking neural networks","volume":"12","author":"Zambrano","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"239","DOI":"10.3389\/fncel.2016.00239","article-title":"Firing frequency maxima of fast-spiking neurons in human, monkey and mouse neocortex","volume":"10","author":"Wang","year":"2016","journal-title":"Front. Cell Neurosci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1162\/089976606775093882","article-title":"Polychronization: Computation with spikes","volume":"18","author":"Izhikevich","year":"2006","journal-title":"Neural Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Pfeil, T., Scherzer, A.-C., Schemmel, J., and Meier, K. (2013, January 4\u20139). Neuromorphic learning towards nano second precision. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6706828"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"30991","DOI":"10.1038\/srep30991","article-title":"The high-efficiency led driver for visible light communication applications","volume":"6","author":"Gong","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain Computer Interfaces, A Review","volume":"12","author":"Alonso","year":"2012","journal-title":"Sensors"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6115","DOI":"10.1038\/s41598-019-41895-7","article-title":"BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains","volume":"9","author":"Jiang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Grau, C., Ginhoux, R., Riera, A., Lam Nguyen, T., Chauvat, H., Berg, M., Amengual, J., Pascual-Leone, A., and Ruffini, G. (2014). Conscious brain-to-brain communication in humans using non-invasive technologies. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105225"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:29:31Z","timestamp":1760178571000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,27]]},"references-count":62,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216119"],"URL":"https:\/\/doi.org\/10.3390\/s20216119","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,10,27]]}}}