G. Theraulaz

The paper investigates how human groups use stigmergic interactions in a rating system to find hidden values, finding cooperation and deception in competitive settings.

This study shows that varying light intensity affects social interactions and collective behavior in schooling fish by altering their responses to obstacles and group motion patterns.

This paper introduces a deep learning model to predict long-term collective behavior in fish and compares it to analytical models and experiments.

This paper develops a computational model to simulate how extracellular matrix composition affects lymphocyte motility patterns through cell-matrix adhesion dynamics.

This paper uses a data-driven fish school model to study how perceptual and cognitive factors affect collective swimming patterns in Hemigrammus rhodostomus, finding that minimal alignment and attraction are required for schooling, and that stronger interactions or larger group sizes can disrupt coordination.

This study conditions a collective avoidance response in rummy-nose tetra using a shuttle box paradigm with green light, showing successful aversive conditioning, collective escape reactions, and transferability to new environments.

The paper uses computational and robotic models to show that fish schools maintain cohesion with minimal neighbor interaction, specifically by responding to the most influential neighbor.

The paper introduces a data-driven method to reconstruct social interactions in moving animal groups and applies it to shoaling fish to create predictive models of collective behavior.

The paper studies collective U-turns in fish schools, showing that speed decreases trigger these events, which propagate like dominoes and are modeled using an Ising spin model.