Researchers discovered that bees use flight movements to sharpen their brain signals, enabling them to recognise patterns with remarkable accuracy. The sophisticated visual pattern learning abilities of bees, such as differentiating between human faces, have long been understood; however the study's findings shed new light on how pollinators navigate the world with such seemingly simple efficiency.
A digital model of their brain shows that this movement-based perception could improve AI and robotics by emphasising efficiency over massive computing power.
British scientists have built a digital model of a bee's brain that explains how these movements create clear, efficient brain signals, allowing bees to easily understand what they see.
The research into bee brains demonstrates that the neural circuits are optimised to process visual information not in isolation, but through active interaction with the flight movements in the natural environment. This supports a theory that intelligence comes from how the brain, body and the environment work together.
Whilst bees possess brains no larger than a sesame seed, they do not simply see the world for they actively shape what they see through their movements. Here action and perception are deeply intertwined to solve complex problems with minimal resources.
The model shows that bee neurons become finely tuned to specific directions and movements as their brain networks gradually adapt through repeated exposure to various stimuli, refining their responses without relying on associations or reinforcement. This lets the bee's brain adapt to its environment simply by observing while flying, without requiring instant rewards.
This means the brain is incredibly efficient, using only a few active neurons to recognise things, conserving both energy and processing power.
To validate their computational model, the researchers subjected it to the same visual challenges encountered by real bees. In a pivotal experiment, the model was tasked with differentiating between a 'plus' sign and a 'multiplication' sign. The model exhibited significantly improved performance when it mimicked the real bees' strategy of scanning only the lower half of the patterns, a behaviour observed by the research team in a previous study.
This discovery could revolutionise AI and robotics, suggesting that future robots can be smarter and more efficient by using movement to gather relevant information, rather than relying on huge computer networks.
The study, from the University of Sheffield, highlights a big idea: intelligence comes from how brains, bodies and the environment work together. It demonstrates how even tiny insect brains can solve complex visual tasks using very few brain cells, which has major implications for both biology and AI.
This work strengthens a growing body of evidence that animals don't passively receive information -- they actively shape it.
Central to the new discovery are insights into how bees use their flight movements to facilitate remarkably accurate learning and recognition of complex visual patterns could mark a major change in how next-generation AI is developed.
By building a computational model -- or a digital version of a bee's brain -- researchers have discovered how the way bees move their bodies during flight helps shape visual input and generates unique electrical messages in their brains.
These movements generate neural signals that allow bees to easily and efficiently identify predictable features of the world around them. This ability means bees demonstrate remarkable accuracy in learning and recognizing complex visual patterns during flight, such as those found in a flower.
The model not only deepens our understanding of how bees learn and recognise complex patterns through their movements, but it also paves the way for next-generation AI. It demonstrates that future robots can be smarter and more efficient by using movement to gather information, rather than relying on massive computing power.
The scientists hope harnessing nature's best designs for intelligence opens the door for the next generation of AI.
The research appears in the journal eLife, titled "A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees."