Research team
Computational neuroscience
Computational neuroscience is an interdisciplinary field that uses mathematical models and theoretical analysis 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) understanding the mechanisms underlying the progression of neurodegenerative diseases through the study and reproduction of typical patterns of functional connectivity and their disruption;
2) designing and testing personalized non-invasive electrical stimulation protocols (e.g. tACS), focused on the modulation of oscillatory activity and the (de-)synchronization between specific brain areas.
Finally, some of our work focuses on applying brain-inspired processing techniques to address engineering problems.
The lab has developed a publicly available neural network simulator, FNS: https://www.fnsneuralsimulator.org/
Selected publications
- E. Javed, I. Suárez-Mández, G. Susi, J. Verdejo Román, J.M. Palva, F. Maestú, and S. Palva. A shift towards super-critical brain dynamics predicts Alzheimer’s disease progression. The Journal of Neuroscience, 45 (9), 2025. https://dx.doi.org/10.1523/jneurosci.0688-24.202
- J. Cabrera-Álvarez, N.Doorn, F.Maestú, & G.Susi. Modeling the role of the thalamus in resting-state functional connectivity: Nature or structure. PLoS computational biology, 19(8), 2023. https://doi.org/10.1371/journal.pcbi.1011007
- J. Cabrera-Álvarez, L. Stefanovski, L. Martin, G. Susi, F. Maestú, and P. Ritter. A multiscale closed-loop neurotoxicity model of Alzheimer’s disease progression explains functional connectivity alterations. eNeuro, 11(4), 2024. https://doi.org/10.1523/ENEURO.0345-23.2023
- 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. https://doi.org/10.1038/s41598-021-91513-8.
- 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. https://doi.org/10.1016/j.neunet.2020.06.001.
