Office Office 16 (C1/032)
Position CRM Postdoctoral Researcher
Research interests Computational Neuroscience
Group Computational Neuroscience
Cecchini, Gloria

I graduated with both a Bachelor and a Master in mathematics at the University of Florence (Italy). I earned a PhD in physics, at the University of Aberdeen (UK) and Potsdam (Germany), focusing on improving network inference by overcoming statistical limitations.
After the PhD, I moved back to Italy where I did a 2-year postdoc in calcium imaging analysis. In particular, I investigated the impact of focal stroke on brain dynamics by analysing spatio-temporal propagation patterns.
In the past 2 years, I was a postdoc researcher at the University of Barcelona (Spain) working on the role of consequence in a decision-making process, by not only analysing data from humans and non-human primates, but also developing mean-field models of consequential reward-driven decision making.

Selected publications

·      Latency correction in sparse neuronal spike trains. Kreuz T., Senocrate F., Cecchini G., Checcucci C., Allegra Mascaro A. L., Conti E., Scaglione A., Pavone F. S.. Journal of Neuroscience Methods 381, 109703, 2022. DOI: 10.1016/j.jneumeth.2022.109703.

·      Cortical propagation tracks functional recovery after stroke. Cecchini G., Scaglione A., Allegra Mascaro A.L., Checcucci C., Conti E., Adam I., Fanelli D., Livi R., Pavone F.S., Kreuz T.. PLoS Comput Biol 17(5): e1008963, 2021. DOI: 10.1371/journal.pcbi.1008963

·      Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging. Chicchi L., Cecchini G., Adam I., de Vito G., Livi R., Pavone F.S., Silvestri L., Turrini L., Vanzi F., Fanelli D..  J Comput Neurosci 49, 159–174, 2021. DOI: 10.1007/s10827-020-00774-1.

·      Impact of local network characteristics on network reconstruction. Cecchini G., Cestnik R., Pikovsky A., Phys. Rev. E 103, 022305, 2021. DOI: 10.1103/PhysRevE.103.022305.

·      Inferring network structure and local dynamics from neuronal patterns with quenched disorder. Adam I., Cecchini G., Fanelli D., Kreuz T., Livi R., Di Volo M., Allegra Mascaro A.L., Conti E., Scaglione A., Silvestri L., Pavone F.S.. Chaos Solitons & Fractals 140, 110235, 2020. DOI: 10.1016/j.chaos.2020.110235.

·      Iterative procedure for network inference. Cecchini G., Schelter B., Communications in Nonlinear Science and Numerical Simulation, 88, 105286, 2020. DOI: 10.1016/j.cnsns.2020.105286.

·      Analytical approach to network inference: Investigating degree distribution. Cecchini G., Schelter B., Physical Review E, 98, 022311, 2018. DOI: 10.1103/PhysRevE.98.022311.

·      Improving Networks Inference: The Impact of False Positive and False Negative Conclusions about the Presence of Links. Cecchini G., Thiel M., Schelter B., Sommerlade L., Journal of Neuroscience Methods, 307, 31-36, 2018. DOI: 10.1016/j.jneumeth.2018.06.011.