This discovery suggests that future robots could become smarter and more efficient by using movement to gather information, reducing dependence on large computational networks.

Professor James Marshall from the University of Sheffield explains that small, evolved systems can perform complex computations, paving the way for advancements in robotics, self-driving cars, and AI.

The study shows that intelligence emerges from the interaction between brains, bodies, and environments, demonstrating that tiny insect brains can solve complex visual tasks with minimal neural resources.

Overall, understanding insect cognition and neural efficiency can inform the development of smarter, resource-efficient AI and robotics.

The computational model successfully demonstrated that small neural networks could differentiate complex patterns like human faces by mimicking bees' scanning behaviors.

Researchers have developed a digital model of a bee's brain that demonstrates how flight movements influence visual input and neural signals, enabling bees to efficiently identify environmental features.

This study highlights that neural tuning to specific movement directions allows bees to adapt and recognize visual stimuli effectively, conserving energy and processing power.

The research from the University of Sheffield shows that bees use their flight movements to facilitate accurate learning and recognition of complex visual patterns, which could inspire future AI development.

Experts from Queen Mary University of London and the University of Sheffield emphasize that minimal neuron counts can handle complex visual discrimination, challenging traditional views on brain size and intelligence.

Building on previous work on active vision in bees, this research offers deeper insights into how their neural circuits are optimized for environmental interaction without heavy reliance on reinforcement or associations.