Studying information processing in the brain with mathematical methods

Associate Professor Naoki Masuda
(Department of Mathematical Informatics)

Although computational neuroscience and network science seem to be unrelated, there is indeed a deep connection.  The brain is a complex network based on interactions among many neurons.  Meanwhile, the brain underlies social interactions as humans process external stimuli in the brain, learn and memorize them, and behave.  Neural and social networks are both complex networks.

Brain research has evolved by accumulating experimental evidence over decades.  Associate Professor Masuda studies functions of the brain by computationally analyzing its information processing.  The focus is on modeling that would enable predictions and designs of experiments; interplay between experiments and theory is critical for understanding the brain.

A network is composed of connection of multiple nodes. Examples include the Internet, WWW, foodweb, social networks, and neural networks. Information tends to propagate faster in a scale-free network in which the number of neighbors is highly heterogeneous. Such a network is useful for, for example, marketing and e-commerce. It is also a key to the efficient prevention of infectious diseases.