82 citations · 84 across the 3 of their papers we have counts for
19 papers
Learning Graph Cellular Automata
Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we fo…
Learn to Synchronize, Synchronize to Learn
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine…
Input-to-State Representation in linear reservoirs dynamics
Pietro Verzelli, Cesare Alippi, Lorenzo Livi +1
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is…
The Echo Index and multistability in input-driven recurrent neural networks
Andrea Ceni, Peter Ashwin, Lorenzo Livi +1
A recurrent neural network (RNN) possesses the echo state property (ESP) if, for a given input sequence, it ``forgets'' any internal states of the driven (nonautonomous) system and…
Recurrence Quantification Analysis of Dynamic Brain Networks
Marinho A. Lopes, Jiaxiang Zhang, Dominik Krzemiński +4
Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based…
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon, Cesare Alippi, Lorenzo Livi
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding natura…