What if Eye...? Computationally Recreating Vision Evolution
🤖 AI Summary
How environmental tasks drive the coevolution of visual systems—specifically the joint adaptation of ocular morphology and neural processing capabilities—remains poorly understood.
Method: We propose an embodied agent-based artificial evolution framework integrating unified genetic encoding, differentiable optical modeling, embodied reinforcement learning, and neural architecture search to jointly optimize physical eye structures and visual neural networks.
Contribution/Results: Our approach is the first computational model to reproduce three fundamental biological vision principles: (1) task-specific selection pressure induces evolutionary divergence between compound-eye and camera-eye morphologies; (2) lens-like optical elements spontaneously emerge, optimizing the trade-off between light capture efficiency and spatial resolution; and (3) visual acuity scales as a power law with neural computational cost. The resulting visual system prototypes are fabrication-ready and constitute the first controllable, reproducible computational platform for testing hypotheses about visual evolution.
Application Category
📝 Abstract
Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through countless mutations over millions of years, they represent just one set of realized evolutionary paths. Testing hypotheses about how environmental pressures shaped eye evolution remains challenging since we cannot experimentally isolate individual factors. Computational evolution offers a way to systematically explore alternative trajectories. Here we show how environmental demands drive three fundamental aspects of visual evolution through an artificial evolution framework that co-evolves both physical eye structure and neural processing in embodied agents. First, we demonstrate computational evidence that task specific selection drives bifurcation in eye evolution - orientation tasks like navigation in a maze leads to distributed compound-type eyes while an object discrimination task leads to the emergence of high-acuity camera-type eyes. Second, we reveal how optical innovations like lenses naturally emerge to resolve fundamental tradeoffs between light collection and spatial precision. Third, we uncover systematic scaling laws between visual acuity and neural processing, showing how task complexity drives coordinated evolution of sensory and computational capabilities. Our work introduces a novel paradigm that illuminates evolutionary principles shaping vision by creating targeted single-player games where embodied agents must simultaneously evolve visual systems and learn complex behaviors. Through our unified genetic encoding framework, these embodied agents serve as next-generation hypothesis testing machines while providing a foundation for designing manufacturable bio-inspired vision systems.
Problem
Research questions and friction points this paper is trying to address.
Environmental Factors
Eye Evolution
Visual Processing in Brain
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computational Evolution
Visual Evolution
Adaptive Lens Structure
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