30 citations · 31 across the 21 of their papers we have counts for
22 papers
Learning-Based Reconstruction of Optical Properties in Bilayered Media from Single-distance Time-Resolved Reflectance Measurements
Caterina Amendola, Giulia Maffeis, Lorenzo Buffoni +8
The inverse problem of reconstructing optical properties, specifically absorption and scattering coefficients, in layered biological media from time-domain reflectance measurements…
Reverse engineering of mechano-kinetic parameters from stochastic force profiles in heterogeneous ensembles of molecular motors and tracks
Valentina Buonfiglio, Irene Pertici, Pasquale Bianco +2
Heterogeneity in contractile systems of molecular motors interacting with their tracks plays a key role in numerous cellular physiological and pathological processes. However, its…
Optimal Navigation on Simplicial Complexes
Diego Febbe, Duccio Fanelli, Gianluca Peri +1
The navigation time and optimal search strategies deriving from random dynamical processes on binary graphs have been extensively explored and analyzed, being of prominent interest…
Spectral Higher-Order Neural Networks Have Sharp Expressivity Bounds
Gianluca Peri, Diego Febbe, Duccio Fanelli
Neural hypergraphs are a natural generalization of neural networks, the reference models in modern machine learning. Yet, their deployment has proven demanding: the number of weigh…
Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality
Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni +3
We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe is given to explicitly accommo…
Model of Simplicial Complexes with dimension-wise preferential attachment
Diego Febbe, Duccio Fanelli, Timoteo Carletti
Network science is a powerful framework allowing to model complex systems, it is capable to describe and take into account the intricate web of connections existing among the const…
Spectral Higher-Order Neural Networks
Gianluca Peri, Timoteo Carletti, Duccio Fanelli +1
Neural networks are fundamental tools of modern machine learning. The standard paradigm assumes binary interactions (across feedforward linear passes) between inter-tangled units,…
Smart Walkers in Discrete Space
Gianluca Peri, Lorenzo Buffoni, Giacomo Chiti +4
We study the statistical properties of trainable agents moving in discrete space. After introducing the mathematical framework, we first analyze the dynamics of two completely rand…
Generalized Wilson-Cowan model with short term synaptic plasticity
Tommaso Trabocchi, Raffaella Burioni, Lucilla de Arcangelis +1
A generalized version of the Wilson-Cowan (WC) model is proposed which accounts for the evolution of the synaptic resources. Adiabatic elimination of the fast variables is performe…
Train Stochastic Non Linear Coupled ODEs to Classify and Generate
Stefano Gagliani, Feliciano Giuseppe Pacifico, Lorenzo Chicchi +4
A general class of dynamical systems which can be trained to operate in classification and generation modes are introduced. A procedure is proposed to plant asymptotic stationary a…
Spectral Architecture Search for Neural Network Models
Gianluca Peri, Lorenzo Chicchi, Duccio Fanelli +1
Architecture design and optimization are challenging problems in the field of artificial neural networks. Working in this context, we here present SPARCS (SPectral ARchiteCture Sea…
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
Lorenzo Chicchi, Duccio Fanelli, Diego Febbe +4
The Continuous-Variable Firing Rate (CVFR) model, widely used in neuroscience to describe the intertangled dynamics of excitatory biological neurons, is here trained and tested as…
A short introduction to Neural Networks and their application to Earth and Materials Science Science
Duccio Fanelli, Luca Bindi, Lorenzo Chicchi +3
Neural networks are gaining widespread relevance for their versatility, holding the promise to yield a significant methodological shift in different domain of applied research. Her…
Resolving the kinetics of an ensemble of muscle myosin motors via a temperature-dependent fitting procedure
Valentina Buonfiglio, Niccolò Zagli, Irene Pertici +3
A data fitting procedure is devised and thoroughly tested to provide self-consistent estimates of the relevant mechanokinetic parameters involved in a plausible scheme underpinning…
Force and kinetics of fast and slow muscle myosin determined with a synthetic sarcomere-like nanomachine
Valentina Buonfiglio, Irene Pertici, Matteo Marcello +7
Myosin II is the muscle molecular motor that works in two bipolar arrays in each thick filament of the striated (skeletal and cardiac) muscle, converting the chemical energy into s…
Turing patterns on discrete topologies: from networks to higher-order structures
Riccardo Muolo, Lorenzo Giambagli, Hiroya Nakao +2
Nature is a blossoming of regular structures, signature of self-organization of the underlying microscopic interacting agents. Turing theory of pattern formation is one of the most…
Learning in Wilson-Cowan model for metapopulation
Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi +4
The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various…
Estimating Global Input Relevance and Enforcing Sparse Representations with a Scalable Spectral Neural Network Approach
Lorenzo Chicchi, Lorenzo Buffoni, Diego Febbe +3
In machine learning practice it is often useful to identify relevant input features. Isolating key input elements, ranked according their respective degree of relevance, can help t…
Complex Recurrent Spectral Network
Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni +2
This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network (-RSN), an innovative v…
Engineered Ordinary Differential Equations as Classification Algorithm (EODECA): thorough characterization and testing
Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi +2
EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting…
How a student becomes a teacher: learning and forgetting through Spectral methods
Lorenzo Giambagli, Lorenzo Buffoni, Lorenzo Chicchi +1
In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. The above scheme proves particularly relevant when the student net…
Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)
Raffaele Marino, Lorenzo Giambagli, Lorenzo Chicchi +2
A novel approach for supervised classification is presented which sits at the intersection of machine learning and dynamical systems theory. At variance with other methodologies th…