Smaller brains can achieve significant feats, research suggests
Smaller Brains Capable of Highly Sophisticated Calculations
A long-held belief that complex brain computations rely on extensive neural networks has been challenged by recent research focusing on the brains of fruit flies. Contrary to previous assumptions, even small neural networks can perform intricate calculations with impressive accuracy. This development forces scientists to reassess fundamental principles of brain computation.
The ability to track orientation is essential for memory, navigation, and decision-making. Researchers historically believed that accurate position and direction tracking required elaborate neural networks prone to errors in smaller systems. However, studies on fruit flies have upended this notion: These tiny insects possess an internal compass constructed on a relatively small neural network. Remarkably, this system is highly precise.
"The fly's compass is incredibly accurate but built from a limited network, contrary to previous theories," says Ann Hermundstad, a group leader at the HHMI's Janelia Research Campus. Marcella Noorman, a postdoctoral researcher at Janelia, led a study that aimed to understand the mystery behind such a compact yet effective system.
The study findings, published in Nature Neuroscience, have not only solved this puzzle but have also forced a reevaluation of the fundamental principles of brain computation. Formerly, neuroscientists believed that a critical component of orientation tracking, known as a ring attractor network, required a large number of neurons to function properly. These networks generate a "bump" of activity that represents orientation, allowing an animal to maintain its sense of direction without external cues. In larger brains, these systems typically function smoothly. Nevertheless, researchers expected the bump of activity to degrade in smaller brains, leading to navigation errors. However, fruit flies contradict this expectation—their internal compass neurons, located in a region called the central complex, exhibit the same dynamics as ring attractor networks in more extensive species. Even when placed in darkness, these neurons maintain stable activity bumps with astounding accuracy.
"Smaller networks can perform more intricate computations than we previously believed," Noorman explains. "Yet to achieve this, the neurons must be connected with extraordinary precision." Researchers discovered that a network with as few as four neurons could operate as a ring attractor, provided the connections between neurons were meticulously fine-tuned. "There is a trade-off between the number of neurons used for computation and the precision required in their connections," Noorman says. Smaller networks depend on precision, while larger networks can tolerate less accurate connections due to redundancy.
To test this theory, researchers employed two-photon calcium imaging to examine the activity of compass neurons in fruit flies suspended on an air-supported ball. This setup enabled them to observe how the flies maintained their sense of direction, even in total darkness. The results demonstrated that the flies' internal compass remained stable and accurate, defying previous assumptions.
These findings pose challenges to long-standing assumptions about how brains process information. The research implies that small neural networks can execute computational tasks previously thought to necessitate larger, more redundant systems. This discovery may have implications beyond neuroscience, potentially revolutionizing fields like robotics and artificial intelligence (AI). Engineers could develop smaller, quicker, and more energy-efficient computational models by designing AI systems that mimic the efficiency of a fruit fly's brain.
The research opens exciting possibilities for future investigations, particularly in determining whether other species with compact neural architectures employ similar mechanisms. Researchers also aim to explore how additional features, such as sensory inputs, enhance the robustness of these networks. Through continued exploration of fruit flies' head direction systems, scientists may reshape our entire understanding of intelligence.
The compact internal compass of fruit flies challenges the assumption that intricate calculations and information processing require large, error-prone neural networks, suggesting that health-and-wellness and navigation can be achieved with precision in smaller networks.
As evidenced by the fruit fly's internal compass, even science-defying computations can be performed by highly sophisticated, though limited, networks when neuron connections are extraordinarily precise, opening prospects for advancements in health-and-wellness, robotics, and artificial intelligence.
