Modeling and Analysis of Fish Interaction Networks under Projected Visual Stimuli

TL;DR

This paper develops a model to analyze how fish schools respond to external visual stimuli, estimating both internal interaction networks and stimulus effects to better understand collective behavior.

Contribution

It extends previous network estimation models by incorporating external stimulus effects and introduces entropy-based indices for influence bias analysis.

Findings

Effectively quantifies schooling behavior under visual stimuli

Identifies influential individuals within fish groups

Provides real-time metrics for collective dynamics

Abstract

This paper addresses the estimation of a dynamic interaction network, a network of influence among individuals, under projected visual stimuli to quantify the influences of inter-individual interactions and external stimuli on collective behavior. Building upon our previously proposed network estimation model, which assumes a Boids-type model and employs a sparse regression framework to infer inter-individual influence networks from trajectory data, we extend the formulation by introducing a stimulus term. This enables the model to capture how individuals react to and propagate externally projected visual stimuli within the group. The resulting framework allows simultaneous estimation of inter-individual and stimulus-related interaction strengths. We also introduce entropy-based indices to capture the possible biases of individuals' influence. Our experiments with fish schools under projector-based visual stimuli demonstrate the effectiveness of the proposed indices in quantifying schooling behavior and identifying influential individuals within the group, serving as the basis for real-time, interpretable metrics of collective dynamics.