PU
I combine methods of computational modeling, information theory and statistical physics to understand the link between brain structure, dynamics and cognitive functions. More specifically, my activity is focused on the circuit mechanisms regulating brain rhythms and the role of these rhythms for cognitive functions. We design mathematical models of neural circuits guided by anatomical and electrophysiological data. At the same time we develop methods of experimental data analyses to compare model predictions with recordings at large scale in the cortex, such as Local Field Potentials and imaging data. These methods meet bottom-up with top-down approaches in neurosciences, thus linking the scale of neurons to brain functions and dysfunctions. More details can be found at this web page
Year | Authors | Title | Journal | PubMed | |
---|---|---|---|---|---|
2025 | Di Geronimo C, Destexhe A, Di Volo M | Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses | J Comput Neurosci. | ||
2024 | Douchamps V, di Volo M, Torcini A, Battaglia D, Goutagny R | Gamma oscillatory complexity conveys behavioral information in hippocampal networks | Nat Commun | ||
2024 | Nandi MK, Valla M, di Volo M | Bursting gamma oscillations in neural mass models | Front Comput Neurosci | ||
2024 | Goldobin DS, di Volo M, Torcini A | Discrete synaptic events induce global oscillations in balanced neural networks | Phys Rev Lett | ||
2022 | di Volo M, Segneri M, Goldobin DS, Politi A, Torcini A | Coherent oscillations in balanced neural networks driven by endogenous fluctuations | Chaos |