30 citations · 31 across the 21 of their papers we have counts for
7 papers · 1 filter
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…