Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
arXiv:2108.02834
Abstract
Anomalous diffusion occurs at very different scales in nature, from atomic systems to motions in cell organelles, biological tissues or ecology, and also in artificial materials, such as cement. Being able to accurately measure the anomalous exponent associated with a given particle trajectory, thus determining whether the particle subdiffuses, superdiffuses or performs normal diffusion is of key importance to understand the diffusion process. Also, it is often important to trustingly identify the model behind the trajectory, as this gives a large amount of information on the system dynamics. Both aspects are particularly difficult when the input data are short and noisy trajectories. It is even more difficult if one cannot guarantee that the trajectories output in experiments is homogeneous, hindering the statistical methods based on ensembles of trajectories. We present a data-driven method able to infer the anomalous exponent and to identify the type of anomalous diffusion process behind single, noisy and short trajectories, with good accuracy. This model was used in our participation in the Anomalous Diffusion (AnDi) Challenge. A combination of convolutional and recurrent neural networks were used to achieve state-of-the-art results when compared to methods participating in the AnDi Challenge, ranking top 4 in both classification and diffusion exponent regression.
arXiv comment20 pages, 11 figures
References in corpus (31)
- A Critical Review of Recurrent Neural Networks for Sequence Learning
- Random Time-Scale Invariant Diffusion and Transport Coefficients
- Brownian motors
- Ergodic Properties of Fractional Brownian-Langevin Motion
- Stochastic Ergodicity Breaking: a Random Walk Approach
- Quantitative analysis of single particle trajectories: mean maximal excursion method
- Nonergodic Subdiffusion from Brownian Motion in an Inhomogeneous Medium
- Weak ergodicity breaking of receptor motion in living cells stemming from random diffusivity
- Anomalous diffusion: A basic mechanism for the evolution of inhomogeneous systems
- Observation of anomalous diffusion and fractional self-similarity in one dimension
- Elucidating the Origin of Heterogeneous Anomalous Diffusion in the Cytoplasm of Mammalian Cells
- Diffusion-limited reactions in dynamic heterogeneous media
- Machine learning method for single trajectory characterization
- Spectral content of a single non-Brownian trajectory
- Improved estimation of anomalous diffusion exponents in single particle tracking experiments
- Measurement of Anomalous Diffusion Using Recurrent Neural Networks
- Noisy continuous time random walks
- Power spectral density of a single Brownian trajectory: What one can and cannot learn from it
- Finite-time effects and ultraweak ergodicity breaking in superdiffusive dynamics
- A note on confined diffusion
- A Test for Determining a Subdiffusive Model in Ergodic Systems from Single Trajectories
- Fractional kinetics emerging from ergodicity breaking in random media
- Classification of particle trajectories in living cells: machine learning versus statistical testing hypothesis for fractional anomalous diffusion
- Nonergodic Diffusion of Single Atoms in a Periodic Potential
- Anomalous Scaling of Stochastic Processes and the Moses Effect
- Continuous-time random walk for a particle in a periodic potential
- Superdiffusion of quantized vortices uncovering scaling behavior of quantum turbulence
- Nonergodic subdiffusion from transient interactions with heterogeneous partners
- Diffusion through a network of compartments separated by partially-transmitting boundaries
- Objective comparison of methods to decode anomalous diffusion
- Transient subdiffusion from an Ising environment