Tiny insect brain discovery offers blueprint for faster, efficient AI and robots

London: The secret behind insects’ lightning-fast reactions could offer a blueprint for more energy-efficient robots and self-driving cars, according to a new study challenging our understanding of how brains process information.

Published in Nature Communications, the University of Sheffield research shows that house flies and fruit flies do not process visual information passively, as previously believed. Rather than simply watching the world, insects twitch their bodies in sync with what they see. These tiny, jerky movements, such as rapid movements of the eyes called saccades, help their brains receive clearer, faster information about the world around them.

By studying flies’ brains and eyes, observing their behaviour, and building digital simulations, researchers discovered a previously unknown ‘turbo boost’ feature called high-frequency jumping.

While nerves usually send information to the brain at a steady pace, this feature allows an insect's visual system to shift gears during fast movement - tripling the speed of data sent to the brain to effectively eliminate delays. This mechanism allows insects to react in milliseconds, sometimes even before visual signals have been fully delivered.

Beyond biology, the research has implications for artificial intelligence and robotics. Current AI systems often rely on large-scale computation and data processing, which can be slow, energy-intensive and expensive.

In contrast, insect brains achieve superior performance using minimal resources by tightly coupling sensing and action.

This suggests that future AI systems - particularly those used in robotics, autonomous vehicles and real-time decision-making - could be revolutionised by adopting similar principles of movement-driven, adaptive information processing.

Professor Mikko Juusola, senior author of the study from the University of Sheffield’s School of Biosciences and Neuroscience Institute, said: “Our findings reveal a fundamentally new way of thinking about how brains compute information - one where speed and efficiency emerge from active interaction with the environment. We’ve demonstrated how even the smallest brains can solve complex problems at extraordinary speeds.

“It shows that vision is not limited by the speed at which insect brains process information. Instead, the brain automatically speeds up to keep pace with the body, cutting out lag and making sure information flows as quickly as possible.”

The study shows that when an insect makes a sharp turn, its brain ‘jumps’ into a higher gear. This opens up more room for data, allowing the insect to focus on the most important, fast-moving information.

The University of Sheffield’s Dr Jouni Takalo, who led the development of the biophysically realistic statistical model underlying the work, said, “Our model shows how thousands of tiny sensors work together to reshape visual signals. By acting as a team, these sensors can instantly shift their focus to where it’s needed most. This allows the insect to produce fast, reliable reactions even when moving at high speeds in the wild.”

Crucially, this mechanism enables insects to overcome physical and neural constraints that would otherwise limit their perception. This supports behaviours such as high-speed flight, predator avoidance and precise navigation in complex environments.

The findings challenge traditional models of neural processing, which assume that information flows through fixed pathways with built-in delays. Instead, the results support a new framework where sight is a collective effort between an insect's movement, its visual input and its brain's response.

The findings 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, energy-hungry computer networks.

Professor Aurel A Lazar, co-author from Columbia University, New York, said: “Nature shows us that intelligence doesn’t come from processing more data, but from processing the right data at the right time. By integrating movement directly into computation, biological systems achieve extraordinary efficiency.

“These principles could guide the design of faster, more robust and energy-efficient AI systems.”

Lars Chittka, Professor in Sensory and Behavioural Ecology at Queen Mary University of London, said, "Flies don’t see the world like a camera taking snapshots. Their vision is tightly intertwined with action, using motion itself to sharpen perception and speed up neural processing. Understanding how biology achieves this kind of predictive, low‑delay sensing could inspire new approaches in artificial vision and neuromorphic engineering."