Closed-loop real-virtual interactions validate 3D model of social coordination in fish

Area of Science:

• Collective behavior
• Animal social interactions
• Computational biology
• Robotics and AI

Background:

• Understanding collective motion in animal groups is crucial for ecological and evolutionary studies.
• Quantitative models are needed to decipher the rules governing social interactions in animal collectives.
• Previous models often lack direct validation against real-time, complex behaviors.

Purpose of the Study:

• To develop a data-driven, three-dimensional model of pairwise interactions in schooling fish (Hemigrammus rhodostomus).
• To validate the model's predictive power using simulations and a novel biohybrid system.
• To establish a framework for linking empirical data, computational models, and observed behavior.

Main Methods:

• Reconstruction of attraction and alignment rules from two-fish behavioral experiments in a controlled environment.
• Development of a fully three-dimensional, data-driven computational model of fish interactions.
• Integration of the model into a closed-loop virtual reality system for real-time biohybrid experiments.

Main Results:

• Simulations quantitatively reproduced empirical distributions of fish speed, distance, and orientation.
• The biohybrid system demonstrated that the model accurately captures key social interactions for coordinated swimming.
• The study validated the model's ability to predict and replicate complex collective behaviors.

Conclusions:

• The developed model provides a robust framework for understanding and predicting collective animal motion.
• The biohybrid approach offers a powerful tool for validating behavioral models in real-time.
• This research paves the way for creating sophisticated hybrid biological-digital collective systems.