Mouse vs. AI: A Neuroethological Benchmark for Visual Robustness

Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems like mice display remarkable resilience to environmental changes—maintaining stable performance even under degraded or perturbed visual input with minimal exposure.Inspired by this gap, we introduce a novel Bio-Inspired Visual Robustness Benchmark for testing generalization in reinforcement learning agents trained to navigate a virtual environment toward a visually cued target. Participants train agents to perform a visually guided foraging task in a naturalistic 3D Unity environment and are evaluated on their ability to generalize to unseen, ecologically realistic visual perturbations, having been exposed during training only to a single illustrative example: fog.What sets this challenge apart is its biological grounding: real mice performed the same task, and participants receive both behavioral performance data and large-scale neural recordings (19,000+ neurons across visual cortex) for benchmarking. The competition features two tracks: (1) Robustness, assessing generalization across held-out perturbations; and (2) Neural Alignment, evaluating how well agents’ internal representations predict mouse visual cortical activity via a linear readout. We provide the full Unity environment, a fog-perturbed training condition for validation, baseline PPO agents, and a rich multimodal dataset. Track 2 offers the first competition framework for testing whether task-trained agents spontaneously develop brain-like representations—assessed by their ability to predict neural activity recorded from mice during the same behavior. By bridging reinforcement learning, computer vision, and neuroscience through shared, behaviorally grounded tasks, this challenge advances the development of robust, generalizable, and biologically inspired AI.