Quantifying information flow along a stochastic trajectory

TL;DR

This paper introduces a scalable deep-learning approach to estimate stochastic information flow from time-series data, enabling empirical analysis of complex systems and revealing cooperative structures.

Contribution

It presents a novel deep-learning method to compute stochastic information flow, overcoming previous computational challenges and broadening practical applications.

Findings

Successfully applied to a two-particle model, Kuramoto oscillators, and cell trajectories.

Demonstrated SIF as an effective indicator of cooperative structures.

Enabled empirical analysis of information flow in complex systems.

Abstract

Stochastic information flow (SIF) quantifies information flow at the trajectory level, overcoming the limitations of conventional symmetric, ensemble-averaged measures. However, computational difficulties have hindered the empirical application of the SIF. In this work, we propose a scalable deep-learning method for estimating the SIF from general time-series data. Its applications to an exactly solvable two-particle model, Kuramoto oscillators, and empirical trajectories of interacting motile cells demonstrate the utility of SIF as a data-driven indicator of cooperative structures.