Symbolic dynamics of animal interaction

Maurizio Porfiri, Manuel Ruiz Marín

Research output: Contribution to journalArticle

Abstract

Since its introduction nearly two decades ago, transfer entropy has contributed to an improved understanding of cause-and-effect relationships in coupled dynamical systems from raw time series. In the context of animal behavior, transfer entropy might help explain the determinants of leadership in social groups and elucidate escape response to predator attacks. Despite its promise, the potential of transfer entropy in animal behavior is yet to be fully tested, and a number of technical challenges in information theory and statistics remain open. Here, we examine an alternative approach to the computation of transfer entropy based on symbolic dynamics. In this context, a symbol is associated with a specific locomotory bout across two or more consecutive time instants, such as reversing the swimming direction. Symbols encapsulate salient locomotory patterns and the associated permutation transfer entropy quantifies the ability to predict the patterns of an individual given those of another individual. We demonstrate this framework on an existing dataset on fish, for which we have knowledge of the underlying cause-and-effect relationship between the focal subject and the stimulus. Symbolic dynamics offers an intuitive and robust approach to study animal behavior, which could enable the inference of causal relationship from noisy experimental observations of limited duration.

Original languageEnglish (US)
Pages (from-to)145-156
Number of pages12
JournalJournal of Theoretical Biology
Volume435
DOIs
Publication statusPublished - Dec 21 2017

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Keywords

  • Information theory
  • Permutation transfer entropy
  • Robotics
  • Social behavior
  • Transfer entropy
  • Zebrafish

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Modeling and Simulation
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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