Towards Neuro-Inspired Electronic Oscillators Based on The Dynamical Relaying Mechanism

Gianluca Susi, Simone Acciarito, Teodoro Pascual, Alessandro Cristini, Fernando Maestú


Electronic oscillators are used for the generation of both continuous and discrete signals, playing a fundamental role in today’s electronics. In both contexts, these systems require stringent performances such as spectral purity, low phase noise, frequency and temperature stability. In state of the art oscillators the preservation of some of these aspects is jeopardized by specific critical issues, e.g., the sensitivity to load capacitance or the component aging over time. This leaves room for the search of new technologies for their realization. On the other hand, in the last decade electronics has been influenced by a growing number of neuro-inspired mechanisms, which allowed for alternative techniques aimed at solving some classical critical issues.In this paper we present an exploratory study for the development of electronic oscillators based on the neuro-inspired mechanism dynamical relaying, which relies on a structure composed of three delay coupled units (as neurons or even neuron populations) able to resonate and self-organise to generate and maintain a given rhythm with great reliability over a considerable parameter range, showing robustness to noise. We used the recent leaky integrated and fire with latency (LIFL) as neuron model. We have initially developed the mathematical model of the neuro-inspired oscillator, and implemented it using Matlab®; then, we have realized the schematic of such system in PSpice®. Finally, the model has been validated to verify whether it observes the fundamental properties of the dynamical relaying mechanisms described in computational neuroscience studies, and if the circuit implementation presents the same behaviour of the mathematical model.Validation results suggest that the dynamical relaying mechanism can be proficuously taken in consideration as alternative strategy for the design of electronic oscillators.


LIFL neuron model; electronic oscillators; dynamical relaying; spiking neural networks.

Full Text:



E. A. Vittoz, M. G. R. Degrauwe, and S. Bitz, "High-performance crystal oscillator circuits: theory and application," in IEEE Journal of Solid-State Circuits, vol. 23, no. 3, pp. 774-783, June 1988.

A. S. Sedra, "The current conveyor: history and progress," IEEE International Symposium on Circuits and Systems, Portland, OR, 1989, pp. 1567-1571 vol.3.

W. Pang, R. C. Ruby, R. Parker, P. W. Fisher, M. A. Unkrich, and J. D. Larson, "A Temperature-Stable Film Bulk Acoustic Wave Oscillator," in IEEE Electron Device Letters, vol. 29, no. 4, pp. 315-318, April 2008.

M.H. Perrott, J.C. Salvia, F.S.Lee., A.Partridge et al. "A Temperature-to-Digital Converter for a MEMS-Based Programmable Oscillator With Frequency Stability and Integrated Jitter," in IEEE Journal of Solid-State Circuits, vol. 48, no. 1, pp. 276-291, Jan. 2013.

M. Zhu and J. L. Hall, "Short and long term stability of optical oscillators," Proceedings of the 1992 IEEE Frequency Control Symposium, Hershey, PA, USA, 1992, pp. 44-55. doi: 10.1109/FREQ.1992.270036.

A.Simonetta, M.C. Paoletti, "Designing Digital Circuits in Multi-Valued Logic," in International Journal on Advanced Science, Engineering and Information Technology, vol.8, n.4, 2018.

F.Alibart, S.Pleutin, O.Bichler, C.Gamrat, T.Serrano-Gotarredona, B.Linares-Barranco, D.Vuillaume, “A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing,” in Adv.Funct.Matter, vol.22, 2012.

Y. Hendrawan and D.F.Al Riza, “Machine Vision Optimization using Nature-Inspired Algorithms to Model Sunagoke Moss Water Status,” in International Journal on Advanced Science, Engineering and Information Technology, vol.6, n.1, 2016.

I. Fischer, R. Vicente, J.M. Buldu´, M. Peil, C.R. Mirasso, M. C. Torrent, J. Garcia-Ojalvo, “Zero-Lag Long-Range Synchronization via Dynamical Relaying,” in Phis Rev Lett, vol.97 n.12. Sept. 2006.

R. Vicente, L.L. Gollo, C.R. Mirasso, I. Fischer, and G. Pipa, “Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays,” Proceedings of the National Academy of Sciences, vol.115, n.44, 2008.

S. Baillet, “Magnetoencephalography for brain electrophysiology and imaging,” in Nature Neuroscience 20, pp.327–339, 2017.

L. Gollo, C. Mirasso, O. Sporns, M. Breakspear, “Mechanisms of Zero-Lag Synchronization in Cortical Motifs,” in Plos Computational biology, 2014

X. Zhao F. Liu, J.Wang and T.Li, “Evaluating Influential Nodes in Social Networks by Local Centrality with a Coefficien.” ISPRS Int. J. Geo-Inf. 2017.

A. Detti, L. Bracciale, P. Loreti, G. Rossi, N. Blefari Melazzi, “A cluster-based scalable router for information-centric networks,” in Computer networks, pp.24-32, vol .142, 2018.

A. Detti, M.Orru, R.Paolillo, G.Rossi, P. Loreti, L.Bracciale, N. Blefari Melazzi, “Application to information centric networking to nosql database,” in 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2017.

I. Gomes Da Silva, J.M. Buldu, C.R. Mirasso, J.Garcia Ojalvo, “Synchronization by dynamical relaying in electronic circuit arrays,” in Chaos 16, 2006

A.Viriyopase, I.Bojak, M.Zeitler, and S. Gielen, “When long-range zero-lag synchronization ios feasible in cortical networks,” in Frontiers in Computational neuroscience, p.49, vol.6, 2012.

M. Salerno, G. Susi, and A. Cristini, “Accurate latency characterization for very large asynchronous spiking neural networks,” in M. Pellegrini, A. L. N. Fred, J. Filipe, and H. Gamboa, editors, BIOINFORMATICS 2011 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, pages 116–124. SciTePress, 2011.

G. C. Cardarilli, A. Cristini, L. Di Nunzio, M. Re, M. Salerno, and G. Susi, “Spiking neural networks based on LIF with latency: Simulation and synchronization effects,” 2013 Asilomar Conference on Signals, Systems and Computers, pages 1838–1842, Pacific Grove, CA, USA, 2013. IEEE.

A. Cristini, M. Salerno, and G. Susi, “A continuous-time spiking neural network paradigm” in S. Bassis, A. Esposito, and F. C. Morabito, editors, Advances in Neural Networks: Computational and Theoretical Issues, pages 49–60. Springer International Publishing, 2015. ISBN 978-3-319-18163-9. doi: 10.1007/978-3-319-18164-6 6.

G. Susi, A. Cristini, and M. Salerno, “Path multimodality in Feedforward SNN module, using LIF with latency model,” in Neural Network World, 26(4): 363–376, 2016.

G. Susi, L. Anton Toro, L.Canuet, M.E. Lopez, F. Maestu, C.R. Mirasso, and E.Pereda, “A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency, and heterosynaptic STDP.” in Frontiers in Neuroscience, 12:780, 2018. ISSN 1662-453X. doi: 10.3389/fnins.2018.00780.

S.Acciarito, A.Cristini, L.Di Nunzio, G.M.Khanal, G.Susi, “An aVLSI driving circuit for memristor-based STDP.” IEEE Prime, Lisbon, 2016

S. Acciarito, G.C. Cardarilli, A. Cristini, L. Di Nunzio, R. Fazzolari, G.M. Khanal, M. Re, and G. Susi, “Hardware design of LIF with latency neuron model with memristive STDP synapses,” in Integration, the VLSI Journal, 59: 81–89, 2017. ISSN 0167-9260

G. Buzsaki, & A. Draguhn, “Neuronal oscillations in cortical networks.” Science, 304(5679), 1926–1929, 2004.

G.Susi, Frutos-Lucas, G. Niso, S.M. Ye-Chen, L.Antón Toro, B.N.Chino Vilca, F.Maestú, “Healthy and Pathological Neurocognitive Aging: Spectral and Functional Connectivity Analyses Using Magnetoencephalography.” in Oxford Research Encyclopedia of psychology and aging. Oxford University Press, 2019.

W.O. A.S. Wan Ismail, M. Hanif, S. B. Mohamed, Noraini Hamzah, Zairi Ismael Rizman, “Human Emotion Detection via Brain Waves Study by Using Electroencephalogram,” in International Journal on Advanced Science, Engineering and Information Technology, vol.6, n.6, 2016.

G.M. Khanal, S. Acciarito, G.C. Cardarilli, A. Chakraborty, L.D. Nunzio, R. Fazzolari, A. Cristini, M. Re and G. Susi. “Synaptic behavior in ZnO–rGO composites, thin film memristor,” in Electronics Letters, vol.53, n.5, 2017.

S.S. Saharuddin, N. Murli, M. Azani Hasibuan, “Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network,” in International Journal on Advanced Science, Engineering and Information Technology, vol.8, n.6, 2018.

Yuhandri, S.Madenda, E.Prasetyo Wibowo, Karmilasari, “Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval,” in International Journal on Advanced Science, Engineering and Information Technology, vol.7, n.6, 2017.

A.A. Amri, A.Ritahani Ismail, A.Ahmad Zarir, “Convolutional Neural Networks and Deep Belief Networks for Analysing Imbalanced Class Issue in Handwritten Dataset,” in International Journal on Advanced Science, Engineering and Information Technology, vol.7, n.6, 2017.

T.T. Khuat, M.H. Le, “An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem,” in JOIV - International Journal on informatics visualization, vol.1, n.2, 2017.

G. Lo Sciuto, G. Susi, G. Cammarata, and G. Capizzi. A spiking neural network-based model for anaerobic digestion process. In IEEE 23rd International Symposium on power electronics, electrical drives, automation and motion (SPEEDAM), pages 1838–1842, Anacapri, Italy, 2016. IEEE.

S. Brusca, G. Capizzi, G. Lo Sciuto, and G. Susi. A new design methodology to predict wind farm energy production by means of a spiking neural network based-system. International Journal of Numerical Modelling: Electronic Networks, Devices, and Fields, 7 2017.

M.S. Meon, M.A. Anuar, M.H.M.Ramli, W.Kuntjoro and Z. Muhammad, “Frame Optimization using Neural Network,” in International Journal on Advanced Science, Engineering and Information Technology, vol.2, n.1, 2012.

T.T. Nakagawa, M.Woolrich, H.Luckhoo, M.Joensson, H.Mohseni, M.L.Kringelbach, V.Jirsa, G.Deco, “How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest,” in Neuroimage, vol.15, n.87, 2014.

A.Mazzoni, H.Linden, H.Cuntz, A.Lansner, S.Panzeri, G.T.Einevoll, “Computing the Local Field Potential from Integrate and Fire Network Models,” in PLOS Computational Biology, 2015.

S.Luck, A.Pikovski, 2011 “Dynamics of multi-frequency oscillator ensembles with resonant coupling” in Physics letters A, vol. 375, issues 28-29, 2011.

R.Giuliano, F.Mazzenga, A.Neri, A.M.Vegni, “Security Access Protocols in IoT Capillary Networks,” IEEE Internet of Things Journal 4 (3), 645-657, 2017.

R.Giuliano, F.Mazzenga, A.Neri, A.M.Vegni, “Security Access Protocols in IoT Capillary Networks with heterogenous non-IP terminals,” in 2014 IEEE International Conference on Distributed Computing in Sensor, 2014.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development