That coordination means the regions are not evolving independently. "Rather," says Imam, "it's a coordinated expansion of these regions across species." The limbic system behaves more like an integrated network than a collection of unrelated structures.

Why do two wiring styles matter?

The explanation centers on how brain systems are wired before birth, and the two systems differ sharply in their layout.

• The neocortex uses spatial maps. Brain regions that process nearby parts of the body — such as the thumb and index finger — also sit near one another. Sight and sound systems show similar spatial organization.
• The limbic system uses distributed patterns. Its wiring works more like a bar code, with spread-out patterns of activity representing particular smells or complex memories.

To test whether these differences come from built-in architecture or from learning through experience, the researchers turned to artificial neural networks. When they built an AI network with localized, spatial connections, it was naturally well suited to processing vision, sound, and touch. Distributed "barcode-style" networks, in contrast, were necessary for strong performance on smell recognition and memory.

How does the competition for brain space work?

Brains have limited resources. Space and energy are finite, so natural selection may favor whichever wiring system best helps an animal survive in its environment.

To test this, the team built a multimodal artificial network in which spatial and distributed systems competed for "real estate." The results mirrored real biology:

• When the simulated environment rewarded smell, every region within the distributed system expanded while the neocortex became smaller.
• When vision was favored instead, the pattern reversed.

This trade-off matches striking differences among real animals. The nine-banded armadillo, which depends heavily on smell, has a very large limbic system. The squirrel monkey, which relies strongly on vision, has a brain dominated by the neocortex.

Across the 182 species in the study, the evidence points one way: brain evolution is less about adding progressively newer layers of logic, and more about shifting space between wiring systems according to survival needs.

What could this mean for AI?

The principle may extend beyond biology. Today's artificial neural networks depend on enormous training datasets — what Imam calls "nurture." Brains do not work that way.

"Today's artificial neural networks are trained by vast amounts [of] data — it's about nurture," says Imam. "But the brain is not a blank slate that gets trained by experience. It is a mix of nature and nurture, and the nature is that pre-wired architecture."

If engineers can reproduce some of that built-in organization in AI, they may be able to build systems that learn more like biological brains and require far less training data and energy. "We could translate that architecture to AI systems to make it more brain-like, or make it learn or function as efficiently as the brain," Imam says.

What are the study's limits?

The findings rest on comparisons across species and on simulations using artificial networks, not on direct experiments inside living brains. The model describes a plausible mechanism — a competition for limited brain space — that fits the observed patterns, but the results are preliminary in the sense that they reinterpret evolutionary data rather than settle the question through direct manipulation.

Even so, the study offers the first coherent answer to why such different limbic circuits get grouped together: they share a wiring style, one that evolution expands or contracts as a unit. The National Science Foundation supported the work.