Hidden structures of information transport underlying spiral wave dynamics
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2017_AshikagaH_Chaos.pdf
README.md
eulerian_analysis.m
generate_data.m
infoflo.m
lagrangian_analysis.m
license
mporange.m
mppink.m
s4_stim.mat

README.md

Hidden structures of information transport underlying spiral wave dynamics

The original paper is here.

How to cite

Please cite the following paper when you use the code in this repo:

Ashikaga H and James RG. Hidden structures of information transport underlying spiral wave dynamics. Chaos 27: 013106, 2017

Dependencies

  1. MATLAB - Tested in MATLAB version 2017a with OSX 10.12.5 Sierra

  2. Function rm_spirals.m, a MATLAB implementation of the Rogers-McCulloch model in two dimensions (2-D) from Rogers-McCulloch repo.

  3. Java Information Dynamics Toolkit JIDT > v1.3.1

Installation

Clone the github repository.

$ git clone https://github.com/ashikagah/spiral-lcs

Usage

  1. Generate time series of spiral waves and information flow

In MATLAB command window,

>> generate_data

It uses the function rm_spirals.m to create sequential stimulations according to the stimulation file s4_stim.mat to induce four spiral waves in a 2-D lattice. It saves the time series of the excitation variable (ts) in a file orig60.mat and its binarized time series in a file bi60.mat. It also creates and saves the time series of information flow [uo, vo] in a file uvo60.mat. The whole process takes several hours, depending on the system used.

  1. Perform Eulerian analysis of information flow

In MATLAB command window,

>> eulerian_analysis

It shows Shannon entropy, instantaneous information flow and total information flow over time, all in an Eulerian perspective.

  1. Perform Lagrangian analysis of information flow

In MATLAB command window,

>> lagrangian_analysis

It shows the forward trajectory of information flow and the Lagrangian coherent structures in an Lagrangian perspective.

Spatial domain

  • Matrix size: 120 x 120
  • Grid spacing: 0.99 mm
  • Grid size: 11.9 x 11.9 cm

Licence

MIT

References

  1. Lizier JT. JIDT: An information-theoretic toolkit for studying the dynamics of complex systems. Frontiers in Robotics and AI 1:11, 2014 html