activity
20242026
most citedChaos in high-dimensional dynamical systems with tunable non-reciprocity

1 citations · 1 across the 3 of their papers we have counts for

collaborators

8 papers

cond-mat.dis-nn2026

Learning limit cycles via Hebbian synaptic plasticity

Samantha J. Fournier, Luca Vincenzo Spallanzani, Pierfrancesco Urbani

We investigate high-dimensional, non-linear dynamical systems when exposed to incoherent periodic inputs and Hebbian-like synaptic plasticity. Our findings reveal a striking phenom…

cond-mat.dis-nn2026

Theory of learning of high-dimensional controlled non-linear dynamical systems (I): models and methods

Pierfrancesco Urbani

Neural ordinary differential equations (neural ODEs) have rapidly gained prominence as a powerful and unifying framework for conceptualizing artificial neural networks, elegantly c…

cond-mat.dis-nn20261 cited

Chaos in high-dimensional dynamical systems with tunable non-reciprocity

Samantha Fournier, Pierfrancesco Urbani

High-dimensional dynamical systems of interacting degrees of freedom are ubiquitous in the study of complex systems. When the directed interactions are totally uncorrelated, suffic…

cond-mat.dis-nn2026

High-dimensional dynamical systems: co-existence of attractors, phase transitions, maximal Lyapunov exponent and response to periodic drive

Samantha J. Fournier, Pierfrancesco Urbani

We study the dynamical properties of a broad class of high-dimensional random dynamical systems exhibiting chaotic as well as fixed point and periodic attractors. We consider cases…

cond-mat.dis-nn2025

Non-reciprocal interactions and high-dimensional chaos: comparing dynamics and statistics of equilibria in a solvable class of models

Samantha J. Fournier, Alessandro Pacco, Valentina Ros +1

We investigate a model of high-dimensional dynamical variables with all-to-all interactions that are random and non-reciprocal. We characterize its phase diagram and show that the…

stat.ML2025

Dynamical Decoupling of Generalization and Overfitting in Large Two-Layer Networks

Andrea Montanari, Pierfrancesco Urbani

Understanding the inductive bias and generalization properties of large overparametrized machine learning models requires to characterize the dynamics of the training algorithm. We…