a-real-time-interactive-system-for-studying-confrontational-pursuit

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Predatory pursuit behavior involves a series of important physiological processes, such as locomotion, learning, and decision-making that are critical to ...

Army Medical University

Both primary auditory cortex (A1) and anterior auditory field (AAF) are core regions of auditory cortex of many mammalians. While the function of A1 has been well documented, the role of AAF in sound related behavioral remain largely unclear. Here in adult rats, sound cued fear conditioning paradigm, surgical ablation, and chemogenetic manipulations were used to examine the role of AAF in fear related sound context recognition. Precise surgical ablation of AAF cannot block sound cued freezing behavior but the fear conditioning became non-selective to acoustic cue. Reversible inhibition of AAF using chemogenetic activation at either training or testing phase can both lead to strong yet non-selective sound cued freezing behavior. These simple yet clear results suggested that in sound cued fear conditioning, sound cue and detailed content in the cue (e.g., frequency) are processed through distinct neural circuits and AAF is a critical part in the cortex dependent pathway. In addition, AAF is needed and playing a gating role for precise recognition of sound content in fear conditioning task through inhibiting fear to harmless cues.

Anterior Auditory Field Is Needed for Sound Categorization in Fear Conditioning Task of Adult Rat.

Escape is one of the most essential behaviors for an animal's survival because it could be a matter of life and death. Much of our current understanding of the neural mechanisms underlying escape is derived from the looming paradigm, which mimics a diving aerial predator. Yet, the idea of the looming paradigm does not account for all types of threats like lions hunting antelopes or cats stalking mice. Escape responses to such terrestrial threats may require different strategies and neural mechanisms.

A method for studying escape behavior to terrestrial threats in rodents.

Confrontations between predator and prey, driven by the innate survival instincts in both predator and prey, constitute the most significant form of competition in evolution. Yet, understanding how survival skills can benefit from such confrontations remains limited, despite its critical importance for animal survival. We have developed an interactive platform to investigate confrontations between a hungry mouse and an escaping bait. This robotic bait is controlled magnetically through a closed-loop system to continually evade the approaching mouse. Meanwhile, the mouse must capture the escaping bait to receive a food reward. Through analysis of angles, speeds and other kinematic parameters of both the mouse and the bait, we observed that confrontation experiences can notably enhance mice performance. Compared with novice mice, veteran mice enhanced predation efficiency primarily by optimizing the pursuit phase, significantly reducing time costs, mainly by minimizing pauses in movement. Additionally, experience strengthened the navigation strategies used by mice to better track evading bait. Finally, we validated the impact of empirically induced changes in speed distribution and pursuit methods on predation efficiency through modeling of the pursuit phase. In conclusion, this study reveals that confrontation experience could improve pursuit strategy in mice by altering the speed control and pursuit method, providing new insights into these crucial behavioral interactions in nature.

Confrontation experience against escaping bait improves pursuit strategy in mice.

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.

Article	Year
Anterior Auditory Field Is Needed for Sound Categorization in Fear Conditioning Task of Adult Rat. Frontiers in neuroscience\| PubMed ID: 31920524	2019
A method for studying escape behavior to terrestrial threats in rodents. Journal of neuroscience methods\| PubMed ID: 38417713	2024
Confrontation experience against escaping bait improves pursuit strategy in mice. The Journal of experimental biology\| PubMed ID: 39606910	2025
PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice. Brain research\| PubMed ID: 40210144	2025

Qiyue Deng

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