A team of researchers at the Graduate School of Informatics, Nagoya University, have brought us one step closer to the development of a neural network with metamemory through a computer-based evolution experiment. This type of neural network could help experts understand the evolution of metamemory, which could help develop artificial intelligence (AI) with a human-like mind. "In order to elucidate the evolutionary basis of the human mind and consciousness, it is important to understand metamemory," says Professor Arita. The team of researchers, which included Professor Takaya Arita, Yusuke Yamato, and Reiji Suzuki of the Graduate School of Informatics developed an artificial neural network model that performed the delayed matching-to-sample task and analyzed its behavior. The neural network was able to examine its memories, keep them, and separate outputs all without requiring assistance or human intervention.

London, June 10 (IANS) Understanding the hierarchical structure of biological networks like human brain -- a network of neurons -- could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. To understand as to why biological networks evolve to be hierarchical, researchers from the University of Wyoming and the French Institute for Research in Computer Science and Automation (INRIA) simulated the evolution of computational brain models, known as artificial neural networks, both with and without a cost for network connections. They found that hierarchy evolves not because it produces more efficient networks, but instead because hierarchically wired networks have fewer connections.