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Algorithm Inspired By Process In Kids’ Brains Could Improve Wireless Networks

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Even though you're undoubtedly smarter than you were as an infant, your brain actually has fewer of the structures that hold memories. Now, researchers are mimicking the process through which the brain "prunes" these structures –known as synapses – during childhood, in order to improve wireless and other engineered networks.

By applying a process similar to that seen in the developing brain, researchers were able to improve routing networks such as airline networks, according to a report published in the journal PLOS Computational Biology

"The neuroscience-based algorithm produced networks that are more efficient and robust than networks designed by other means," lead study author Saket Navlakha of Carnegie Mellon University told Tech Times. "In particular, in the networks created via pruning, the flow of information was more direct, and provided multiple paths for information to reach the same endpoint, which minimized the risk of network failure."

During childhood, your brain prunes away synapses that are less useful so that effective synapses can become stronger, and your brain as a whole can become more efficient. The researchers imaged the brains of mice to study this process and then created an algorithm that can be applied to digital networks, based on their observations. 

Like the network of neurons in your brain, wireless and sensor networks must be adaptive. This process of pruning can help networks adapt more efficiently.

Navlakha notes that this method would likely not be cost-effective for networks that require significant infrastructure, like railways or pipelines. While the algorithm could help make the routes within these networks more efficient, pruning away expensive railroad tracks, for example, would not be very practical. But it could be a valuable method to guide the formation of wireless or sensor networks, according to Navlakha.

"We are excited about 'thinking algorithmically' about the brain as a means to both develop new algorithms in network science and also to help us better understand systems-level information processing in the brain," he said. "We think the brain is ripe with many more lessons to be learned."

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