154 citations · 448 across the 31 of their papers we have counts for
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Run-and-Tumble Particles Learning Chemotaxis
Nicholas Tovazzi, Gorka Muñoz-Gil, Michele Caraglio
Through evolution, bacteria have developed the ability to perform chemotactic motion in order to find nourishment. By adopting a machine learning approach, we aim to understand how…
Learning how to find targets in the micro-world: The case of intermittent active Brownian particles
Michele Caraglio, Harpreet Kaur, Lukas J. Fiderer +4
Finding the best strategy to minimize the time needed to find a given target is a crucial task both in nature and in reaching decisive technological advances. By considering learni…
Quantitative evaluation of methods to analyze motion changes in single-particle experiments
Gorka Muñoz-Gil, Harshith Bachimanchi, Jesús Pineda +33
The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell component…
Learning minimal representations of stochastic processes with variational autoencoders
Gabriel Fernández-Fernández, Carlo Manzo, Maciej Lewenstein +2
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertaint…
Inferring pointwise diffusion properties of single trajectories with deep learning
Borja Requena, Sergi Masó, Joan Bertran +3
In order to characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so,…