Classification, inference and segmentation of anomalous diffusion with recurrent neural networks

Abstract

Countless systems in biology, physics, and finance undergo diffusive dynamics. Many of these systems, including biomolecules inside cells, active matter systems and foraging animals, exhibit anomalous dynamics where the growth of the mean squared displacement with time follows a power law with an exponent that deviates from $1$. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks can be overwhelmingly difficult when only few short trajectories are available, a situation that is common in the study of non-equilibrium and living systems. Here, we present a data-driven method to analyze single anomalous diffusion trajectories employing recurrent neural networks, which we name RANDI. We show that our method can successfully infer the anomalous exponent, identify the type of anomalous diffusion process, and segment the trajectories of systems switching between different behaviors. We benchmark our performance against the state-of-the art techniques for the study of single short trajectories that participated in the Anomalous Diffusion (AnDi) Challenge. Our method proved to be the most versatile method, being the only one to consistently rank in the top 3 for all tasks proposed in the AnDi Challenge.

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Cited by in corpus (18)

• Bayesian deep learning for error estimation in the analysis of anomalous diffusion
• Machine-Learning Solutions for the Analysis of Single-Particle Diffusion Trajectories
• Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
• Characterization of anomalous diffusion through convolutional transformers
• WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)
• Semantic Segmentation of Anomalous Diffusion Using Deep Convolutional Networks
• Unsupervised learning of anomalous diffusion data
• Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
• Variational inference of fractional Brownian motion with linear computational complexity
• Learning minimal representations of stochastic processes with variational autoencoders
• Machine Learning Analysis of Anomalous Diffusion
• Objective comparison of methods to decode anomalous diffusion
• Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
• Change-point detection in anomalous-diffusion trajectories utilising machine-learning-based uncertainty estimates
• Recurrent neural network analysis of single trajectories switching between anomalous diffusion states
• Recovering discrete delayed fractional equations from trajectories
• An empirical method to characterize displacement distribution functions for anomalous and transient diffusion
• Complete Decomposition of Anomalous Diffusion in Variable Speed Generalized Lévy Walks