Thursday, 14 May 2026, 15:00-16:00 UK time

Prof. Manlio De Domenico

Register via: https://cam-ac-uk.zoom.us/meeting/register/Dqfu_S6EThat4col7z1ZQQ#/registration

Towards a theory of network functionality in the brain and beyond

Understanding how brain networks give rise to function remains a central challenge. While connectomics has revealed detailed structural organization, it is unlikely that structure alone determines function; instead, functionality emerges from the interplay between topology, dynamics, and responses to perturbations across scales.

In this talk, I will present a statistical physics framework aimed at developing a predictive theory of network functionality. Using tools from statistical physics and information theory, I define function in terms of information propagation on networks and introduce a density matrix formalism to capture multiscale dynamics. When the underlying dynamics are unknown, maximum entropy principles provide the least-biased description consistent with observations, leading to a generalized thermodynamic perspective.

This approach enables the quantification of functional robustness and predicts macroscopic features of real systems, including the emergence of sparse connectivity as an optimal trade-off between efficiency and diversity, across brain networks and beyond.

References:

Decoding the architecture of living systems

M. De Domenico, Rep. on Progr. Phys. 89, 014601 (2026)

Latent geometry emerging from network-driven processes

A.F. Beretta, D. Zanchetta, S. Bontorin, M. De Domenico 

npj Complexity 2, 37 (2025)

Unraveling the mesoscale organization induced by network-driven processes

G. Barzon, O. Artime, S. Suweis, M. De Domenico, PNAS 121, e2317608121 (2024)

Diversity of information pathways drives sparsity in real-world networks

A. Ghavasieh, M. De Domenico, Nature Physics 20, 512 (2024)

More is different in real-world multilayer networks

M. De Domenico, Nature Physics 19, 1247 (2023)

Spectral entropies as information-theoretic tools for complex network comparison

M. De Domenico, J. Biamonte, Phys. Rev. X 6, 041062 (2016)