PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice

Abstract

Predator-prey interactions exemplify adaptive intelligence refined by evolution, yet replicating these behaviors in artificial systems remains challenging. Here, we introduce PursuitNet, a deep learning framework specifically designed to model the competitive, real-time dynamics of pursuit-escape scenarios. Our approach is anchored by the Pursuit-Escape Confrontation (PEC) dataset, which records laboratory mice chasing a magnetically controlled robotic bait programmed to evade capture. Unlike conventional trajectory datasets, PEC emphasizes abrupt speed changes, evasive maneuvers, and continuous mutual adaptation. PursuitNet integrates a lightweight architecture that explicitly models dynamic interactions and spatial relationships using Graph Convolutional Networks, and fuses velocity and acceleration data to predict change using Temporal Convolutional Networks. In empirical evaluations, it outperforms standard models such as Social GAN and TUTR, exhibiting substantially lower displacement errors on the PEC dataset. Ablation experiments confirm that integrating spatial and temporal features is crucial for predicting the erratic turns and speed modulations inherent to pursuit-escape behavior. Beyond accurate trajectory prediction, PursuitNet simulates pursuit events that closely mirror real mouse-and-bait interactions, shedding light on how innate drives, rather than external instructions, guide adaptive decision-making. Although the framework is specialized for rapidly shifting trajectories, our findings suggest that this biologically inspired perspective can deepen understanding of predator–prey dynamics and inform the design of interactive robotics and autonomous systems.