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publications

Controllability of structural brain networks

Published in Nature Communications, 2015

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Read more

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talks

The Re-evolution of Human Brain

Published:

Professor Gu works for School of Computer Science and Engineering in UESTC, and he holds a PhD in Applied Mathematics and Computational Science from University of Pennsylvania, and has been the candidate of Thirteenth Batch of National 1000 People Project Youth Program. He put forward a control model of brain network, established a link between brain network cognitive control and engineering control model, which provides a feasible new idea for understanding the brain cognitive control function and gains pioneering theoretical achievements, and resulting in a profound significance for intelligent control model. Also being an neurotic system development researchers, he will lead us to explore the frontiers of human potential development. Read more

Functional Controllability on Brain Networks

Published:

In recent years, both network neuroscience and cognitive science have developed vigorously. The network approach provides an analytical perspective of understanding the brain structures and functions, and uncovers the intrinsic correlation between them. However, this family of methods pays more attention on the discovery and pattern recognition in the phenomenon, thus lack a mechanistic explanation of why and how this correlation happens. Read more

Multi-object optimization on structural and functional community structures

Published:

Human brain is a complex system displaying modular characteristics in both structural and functional networks. Although coupled on a certain level, such two types of modular structures remain different in principle where the structural modules provide disciplines of anatomical organization, while the functional modules reflect the assembling principles of statistical association. Read more

teaching

Introduction to Network Neuroscience and Artificial Intelligence

Undergraduate course, UESTC, Computer Science, 2019

Network science is an interdisciplinary field that focuses on the networked objects in physical world, biological systems and social phenomenon. It builds predictive models with properties from the perspective of complex network to investigate problems in information network, internet network, biological network, learning and cognitive network, and social network. This course consists of two sections. In the first section, we will introduce the general theory of complex network. In the second section, we will introduce how to apply the network theory as well as other data mining methods to perform analyses in brain science, and how to understand the principles of brain organization and function. Throughout the learning of this course, we hope that the students can have a glimpse of the frontier of network neuroscience and get familiar of the paradigm of research. Read more

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