Brain-based Algorithms Make for Better Networks

Researchers from the Salk Institute for Biological Studies and Carnegie Mellon University recently determined the rate at which the developing brain eliminates unneeded connections between neurons during early childhood. The findings, published in the journal PLOS Computational Biology, are the latest in a series of studies being conducted in Carnegie Mellon’s Systems Biology Group to develop computational tools for understanding complex biological systems, while applying those insights to improve computer algorithms. “By thinking computationally about how the brain develops, we questioned how rates of synapse pruning may affect network topology and function,” said the assistant professor at the Salk Institute’s Center for Integrative Biology. “We have used the resulting insights to develop new algorithms for constructing adaptive and robust networks in other domains.” Network structure is an important topic for both biologists and computer scientists. In biology, understanding how the network of neurons in the brain organizes to form its adult structure is key to understanding how the brain learns and functions. In computer science, understanding how to optimize network organization is essential to producing efficient interconnected systems. Researchers took the high-level algorithm that explains how neural structures are built during development and used it to inspire an algorithm for an engineered network regarding air-flight patterns and found it was able to create routes to allow passengers to reach their destinations efficiently. To read more about this study, click here.