# Modeling and Analysis of Fish Interaction Networks under Projected Visual Stimuli

arXiv: https://arxiv.org/abs/2603.01682 · HTML: https://mlsanity.com/paper/2603.01682/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents](https://mlsanity.com/paper/2603.28200/) (2026)

## Abstract (arXiv)

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.