Research team
Computational neuroscience
Computational neuroscience is an interdisciplinary field that uses mathematical models and theoretical analyzes to understand the principles that govern the physiology, structure and development of the nervous system and related cognitive abilities.
Specifically, in the laboratory we carry out simulations of brain networks based on real data, aimed at:
1) understand the mechanisms underlying the progression of neurodegenerative diseases through the study and reproduction of typical patterns of functional connectivity and their disruption;
2) design and test personalized non-invasive electrical stimulation protocols (e.g. tACS), focused on modulation of oscillatory activity and synchronization between specific brain areas.
The lab has developed a publicly available neural network simulator, FNS: https://www.fnsneuralsimulator.org/
Recent publications
G. Susi, P. Garcés, E.Paracone, A.Cristini, M.Salerno, F.Maestú, E.Pereda. FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. Sci Rep. 2021 Jun; 11(1):12160.
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 Comput & Applic, 2021.
G. Susi, L. Antón, F. Maestú, C. Mirasso. nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift. Front Neurosci (15) 2021.
G. Capizzi, G.Lo Sciuto, C.Napoli, M.Wozniak, G.Susi. A spiking neural network-based long-term prediction system for biogas production. Neural Networks (129) 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. – Alzheimer’s and Dementia, 2020, 16.
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.
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 , L. Antón Toro ,L.Canuet, M.E.López, F.Maestú, C.R.Mirasso , E.Pereda. 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.
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.
Selected publications
2021
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.
2020
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.
2018
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.
