For decades, scientists believed that complex computations in the brain required vast networks of neurons.
The bigger the brain, the greater its processing power—at least, that was the assumption.
But new research has shattered this idea, revealing that even small brains can perform highly sophisticated calculations with remarkable accuracy.
A Tiny Brain with a Powerful Compass
The brain’s ability to track orientation is essential for memory, navigation, and decision-making.
Traditionally, researchers thought that accurate tracking of position and direction required large, intricate neural networks.
Smaller systems, in contrast, were assumed to be prone to errors—drifting off course or failing to integrate new information.
But recent studies on fruit flies have flipped this belief upside down.
It turns out that these tiny insects possess an internal compass built on a surprisingly small neural network.
And despite its limited size, this system is incredibly precise.
“The fly’s compass is very accurate, but it’s built from a really small network, contrary to what previous theories assumed,” says Ann Hermundstad, a group leader at HHMI’s Janelia Research Campus.
Marcella Noorman, a postdoctoral researcher at Janelia, led a study that sought to understand how such a compact system could perform so well.
The results, published in Nature Neuroscience, have not only solved this mystery but have also forced scientists to rethink the fundamental principles of brain computation.
Challenging the Big Brain Theory
For years, neuroscientists believed that an essential component of orientation tracking—known as a ring attractor network—needed a large number of neurons to function correctly.
These networks create a “bump” of activity that represents orientation, allowing an animal to maintain its sense of direction even without external cues.
In large brains, these systems work smoothly.
But in smaller brains, scientists expected the bump of activity to degrade, leading to errors in navigation.
However, fruit flies defy this expectation. Their internal compass neurons, located in a region called the central complex, exhibit the same dynamics as ring attractor networks in larger species. Even when placed in darkness, these neurons maintain stable activity bumps, tracking the fly’s movements with incredible accuracy.
“Smaller networks and smaller brains can perform more complicated computations than we previously thought,” Noorman explains. “But to do so, the neurons must be connected much more precisely than in larger systems.”
This insight represents a major shift in how scientists view neural computation. It’s not just about size—it’s about precision.
The Secret to Small Brain Efficiency
Noorman and her team wanted to uncover exactly how these small neural networks were pulling off such an impressive feat.
To do so, they stripped down existing models of ring attractor networks, removing additional complexities like biophysical properties.
Initially, Noorman believed that a small network simply couldn’t generate a stable ring attractor.
But when her attempts to prove this failed, she took a different approach.
By shifting her perspective, she discovered that a network with as few as four neurons could function as a ring attractor—provided the connections between neurons were fine-tuned with extreme precision.
“There’s a trade-off between how many neurons you use for computation and how carefully you have to connect them,” Noorman explains.
“Smaller networks rely on precision, whereas larger networks can afford less exact connectivity due to redundancy.”
To test this theory, researchers used two-photon calcium imaging to study the activity of compass neurons in fruit flies tethered to an air-supported ball.
This setup allowed them to track how the flies maintained their sense of direction—even in complete darkness.
The results were astonishing. Despite their small size, the flies’ internal compass remained stable and accurate, defying previous predictions.
What This Means for Neuroscience and Beyond
These findings challenge long-standing assumptions about how brains process information.
They suggest that small neural networks can achieve computational feats once thought to require larger, more redundant systems.
This discovery has implications far beyond neuroscience. Understanding how small networks achieve efficiency could revolutionize fields like robotics and artificial intelligence (AI).
By designing AI systems that mimic the efficiency of a fly’s brain, engineers could develop smaller, faster, and more energy-efficient computational models.
“Fruit flies have provided neuroscientists with a model that demonstrates how small systems can overcome apparent limitations,” Noorman says.
“The precise connectivity observed in these networks may explain their ability to achieve continuous representation without error.”
Could This Apply to Human Brains?
If tiny fly brains can perform advanced computations with just a handful of neurons, could larger brains—including those of humans—be using similar principles?
This research opens up exciting possibilities.
If precision in connectivity is more important than sheer neuron count, then human brains may be capable of far greater computational efficiency than we currently understand.
Next, scientists aim to investigate whether other species with compact neural architectures use similar mechanisms.
They also want to explore how additional features, such as sensory inputs, enhance the robustness of these networks.
Small Brains, Big Potential
The fruit fly’s compact yet powerful internal compass highlights an important trade-off in neural computation: precision versus redundancy.
The discovery that small networks can perform sophisticated computations challenges the idea that bigger brains are always better.
As researchers continue to explore the fly’s head direction system, they hope to uncover even more secrets about how brains—big and small—process information.
And in the process, they may just reshape our entire understanding of intelligence.
