About me

Synthetic spike raster from a reduced Brunel-type network with 2500 neurons. The same network alternates between two parameter settings: SI-like, g=4.5 and eta=0.8; AI-like, g=5 and eta=2. Each setting lasts approximately 16 seconds of animation. Connectivity and neuronal state remain continuous across transitions. Independent Poisson inputs are stationary within each setting. No OU input, imposed bursts, labels or visitor controls. Reduced-motion preferences are respected.

I’m a postdoctoral researcher at the Laboratory of Interdisciplinary Physics and the Padova Neuroscience Center, with a background in the physics of complex systems. I combine mechanistic modelling and data analysis across scales to uncover the principles underlying neural activity and develop experimentally testable predictions. My research explores how neural dynamics support computation, with a particular interest in criticality, oscillations and synchronization.