Scientists at the University of Cambridge in the U.K. have created a self-organizing, artificially intelligent system that uses the same tricks as the human brain to solve specific tasks.

This discovery not only allows for the development of more efficient neural networks within the field of machine learning, but may also provide new insights into the inner workings of the human brain itself. One of the study authors told Newsweek they were “very surprised” by the results.

The human brain and other complex organs develop under a set of restrictions and competing demands. For example, our neural networks must be optimized for information processing but not use up too much energy or resources. These trade-offs shape our brains to create an efficient system that functions within these physical constraints.

“Biological systems commonly evolve to make the most of what energetic resources they have available to them,” co-lead author Danyal Akarca, from the Medical Research Council Cognition and Brain Sciences Unit at the University of Cambridge, said in a statement. “The solutions they come to are often very elegant and reflect the trade-offs between various forces imposed on them.”

Together with co-lead author Jascha Achterberg, a computational neuroscientist from the same department, Akarca and his team created an artificial system with imposed physical constraints intended to model a simplified version of the brain. Their results were published in the journal Nature Machine Intelligence on November 20.

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