Gianluca Susi

Gianluca Susi is an electronics engineer. He received a PhD in sensor and learning systems engineering from the University of Rome "Tor Vergata" in 2012.
Gianluca Susi was adjunct professor of Electrical Circuits at the University of Rome "Tor Vergata", and today he is senior researcher at the UPM-UCM Laboratory of Cognitive and Computational Neuroscience.
His research activity is currently focused on the simulation of spontaneous brain network activity, using both spiking neural networks and neural mass models, to understand and reproduce connectivity transitions during the progression of neurodegenerative diseases.

Other research interests

Data Analysis

Research on and application of data analysis methods in our laboratory focuses on functional connectivity, preprocessing methods, classification, source reconstruction and Bayesian models. We have developed the publicly available connectivity toolbox HERMES.

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Main publications


  • Varotto G, Susi G, Tassi L, Gozzo F, Franceschetti S, Panzica F. Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy. Front Neuroinform. 2021; 15:715421. PubMed ID: 34867255.
  • Santos-Mayo A, Moratti S, de Echegaray J, Susi G. A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity. Biology (Basel). 2021 Aug; 10(8):. PubMed ID: 34440033.
  • Susi G, Garcés P, Paracone E, Cristini A, Salerno M, Maestú F, Pereda E. FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. Sci Rep. 2021 Jun; 11(1):12160. PubMed ID: 34108523.
  • Susi G, Antón-Toro LF, Maestú F, Pereda E, Mirasso C. -A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift. Front Neurosci. 2021; 15:582608. PubMed ID: 33679293.


  • Capizzi G, Lo Sciuto G, Napoli C, Woźniak M, Susi G. A spiking neural network-based long-term prediction system for biogas production. Neural Netw. 2020 Sep; 129:271-279. PubMed ID: 32569855.


  • Susi G, Antón Toro L, Canuet L, López ME, Maestú F, Mirasso CR, Pereda E. A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP. Front Neurosci. 2018; 12:780. PubMed ID: 30429767. PDF file PDF file.

Other publications

A. Giovannetti, G. Susi, P. Casti,  A. Mencattini, S.Pusil, M.E. López, C. Di Natale, E. Martinelli. Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography. Neural Computing and applications (2021).

D. Lopez-Sanz, J. de Frutos Lucas, G.Susi and F. Maestu. Magnetoencephalography in Alzheimer's disease: correlation with current biomarkers. In OXFORD RESEARCH ENCYCLOPEDIA: 50 years of MEG. Oxford University press, 2020.

P. Casti, A. Giovannetti, G. Susi, A. Mencattini, S. Pusil, M.E. López,, C. Di Natale, and E. Martinelli. A deep CNN-based approach for predicting MCI to AD conversion. In 2020 Alzheimer's Association International Conference (ALZ2020), 2020.

G. Susi, I. Suárez Méndez, D. López Sanz, M. E. López García, E. Paracone, E. Pereda, F. Maestu. Hippocampal volume and functional connectivity transitions during the early stage of Alzheimer’s disease: a Spiking Neural Network-based study. 28th Annual Computational Neuroscience Meeting: CNS*2019. - BMC Neuroscience 2019, 20 (Suppl 1).

G. Susi, J. de Frutos Lucas, G. Niso, S.M. Ye Chen, L. Ant´on Toro, B.N. Chino Vilca, and F. Maestu. Healthy and pathological neurocognitive aging: Spectral and functional connectivity analyses using magnetoencephalography. In OXFORD RESEARCH ENCYCLOPEDIA OF PSYCHOLOGY AND AGING. Oxford University press, 2019.

G. Susi, S. Acciarito, T. Pascual, A. Cristini, and F. Maestu. Towards neuro-inspired electronic oscillators based on the dynamical relaying mechanism. International Journal on Advanced Science, Engineering and Information Technology, 9(2), 2019.

G. Susi, F. Bartolacci, and Massarelli M. A computational approach for the understanding of stochastic resonance phenomena in the human auditory system. International Journal on Advanced Science, Engineering and Information Technology, 9(4), 2019.

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. Integration, the VLSI Journal, 59:81-89, 2017.

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.

G.M. Khanal, S. Acciarito, G.C. Cardarilli, A. Chakraborty, L. Di Nunzio, R. Fazzolari, A. Cristini, G. Susi, and M. Re. ZnO-rGO composite thin film resistive switching device: emulating biological synapse behavior. In A. De Gloria, editor, Lecture Notes in Electrical Engineering: Applications in Electronics Pervading Industry, Environment and Society., pages 117-123. Springer International Publishing, 2017.

G.M. Khanal, S. Acciarito, G.C. Cardarilli, A. Chakraborty, L. Di Nunzio, R. Fazzolari, A. Cristini, M. Re, and G. Susi. Synaptic behaviour in ZnOrGO composites thin film memristor. Electronics Letters, 53(5):296-298, 2017.

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

G. Susi. Bio-inspired temporal-decoding network topologies for the accurate recognition of spike patterns. Transactions on Machine Learning and Artificial Intelligence, 3(4):27-41, 2015.

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. Smart Innovation, Systems and Technologies, volume 37, pages 49{60. Springer International Publishing, 2015

M. Salerno, G. Susi, A. Cristini, Y. Sanfelice, and A. D ’Annessa. Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications. ACEEE Int. J. on Information Technology, 3(1), 2013.

G.Susi. Asynchronous spiking neural networks: paradigma generale e applicazioni. TEXmat, 2013.

M. Salerno, M. Re, A. Cristini, G. Susi, M. Bertola, E. Daddario, and F. Capobianco. Audinect: an aid for the autonomous navigation of visually impaired people, based on virtual interface. International journal of Recent Trends in Human Computer Interaction, 4(1):25-33, 2013.

Electronic versions are provided as a professional courtesy to ensure timely dissemination of academic work for individual, noncommercial purposes. Copyright (and all rights therein) resides with the respective copyright holders, as stated within each paper. These files may not be reposted without permission.