Bio-inspired information and communications technologies with adaptability to environmental changes

Self-organizing control is one of the key ideas for controlling large-scale networks. In self-organizing networks, each node in a network uses local information to make decisions. It is known that decisions made here by system components at the local level and interactions among components allow the system to respond to changes in the environment without external or centralized control. The behavior of living organisms and phenomena in nature are known to have self-organizing characteristics, and such characteristics can be successfully exploited to realize adaptive information and communication technology.

Information processing technology inspired from the information processing model of the human brain

The human brain is known for its power-saving and high information processing capabilities. By utilizing such brain mechanisms, it is expected to realize networks with features such as high communication efficiency, robustness, adaptivity to fluctuation, and adaptive evolution. We are analyzing networks in the human brain, generating network structures with these features, and studying information and communication technology that makes appropriate decisions in response to uncertain information by utilizing information processing models of the brain's recognition.