activity
20152026
most citedLarge language models surpass human experts in predicting neuroscience results

134 citations · 306 across the 23 of their papers we have counts for

collaborators
Showing 2025Show all

5 papers · 1 filter

q-bio.QM2025★ 1 cited

Connectome brain fingerprinting: terminology, measures, and target properties

Matteo Fraschini, Matteo Demuru, Daniele Marinazzo +1

Distinguishing one person from another (what biometricians call recognition) is extremely relevant for different aspects of life. Traditional biometric modalities (fingerprint, fac…

q-bio.QM2025

Localizing synergies of hidden factors across complex systems: resting brain networks and HeLa gene expression profile as case studies

Marlis Ontivero-Ortega, Gorana Mijatovic, Luca Faes +2

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Eve…

cs.CL2025

Cognitive networks highlight differences and similarities in the STEM mindsets of human and LLM-simulated trainees, experts and academics

Edith Haim, Lars van den Bergh, Cynthia S. Q. Siew +3

Understanding attitudes towards STEM means quantifying the cognitive and emotional ways in which individuals, and potentially large language models too, conceptualise such subjects…

stat.ME2025

Decomposing Multivariate Information Rates in Networks of Random Processes

Laura Sparacino, Gorana Mijatovic, Yuri Antonacci +4

The Partial Information Decomposition (PID) framework has emerged as a powerful tool for analyzing high-order interdependencies in complex network systems. However, its application…

stat.ME2025★ 1 cited

Partial Information Rate Decomposition

Luca Faes, Laura Sparacino, Gorana Mijatovic +4

Partial Information Decomposition (PID) is a principled and flexible method to unveil complex high-order interactions in multi-unit network systems. Though being defined exclusivel…