Xiaolin Hu

A short bio I got my PhD degree in Automation and Computer-Aided Engineering from The Chinese University of Hong Kong in 2007. Then I became a post-doc researcher at the Department of Computer Science and Technology, Tsinghua University. Since 2009, I have been a faculty member of this department. My current research interests include artificial neural networks and computational neuroscience. I'm a Senior Member of IEEE and an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems. Education background: Bachelor of Automotive Engineering, Wuhan University of Technology, Wuhan, China, 2001; Master of Automotive Engineering, Wuhan University of Technology, Wuhan, China, 2004; Ph.D. in Automation and Computer-Aided Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China, 2007. Social service Associate Editor, IEEE Transactions on Neural Networks and Learning Systems Areas of Research Interests/ Research Projects Artificial Neural Networks Computational Neuroscience Courses Neural and Cognitive Computation (80240642,Graduate). Research Projects National Natural Science Foundation of China: Design of Recurrent Neural Network Groups for Optimization based on KKT Conditions (2009-2011); National Natural Science Foundation of China: Deep Learning Networks based on Sparse Coding Models (2013-2016) Research Status I'm working in the interaction between computer science and cognitive neuroscience. On one hand, I'm interested in unraveling the secrets of the brain, especially the mechanisms of sensory information processing and decision making. Two main techniques I rely on are hierarchical computational models and Bayes theory. Another technique I'm now trying to use is functional magnetic resonance imaging (fMRI) combined with machine learning methods. On the other hand, I'm interested in brain-inspired computing methods. Currently I'm trying to integrate more neuroscience knowledge into deep learning models for boosting their performances as well as efficiency for object recognition and detection. In addition, I'm involved in a neuromorphic hardware project, which aims at developing an intelligent and energy-efficient brain-like system. My previous interest is the design of recurrent neural network for solving optimization problems, which also lies in this direction. Honors And Awards Ministry of Education, The People's Republic of China: Neurodynamic Optimization: Models and Applications, Natural Science Award, 1st Class (2012); Tsinghua University: Excellent Postdoctoral Fellow (2009); ICONIP 2012: Best Paper Award (2012).

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Latest work with IntechOpen by Xiaolin Hu

The concept of neural network originated from neuroscience, and one of its primitive aims is to help us understand the principle of the central nerve system and related behaviors through mathematical modeling. The first part of the book is a collection of three contributions dedicated to this aim. The second part of the book consists of seven chapters, all of which are about system identification and control. The third part of the book is composed of Chapter 11 and Chapter 12, where two interesting RNNs are discussed, respectively.The fourth part of the book comprises four chapters focusing on optimization problems. Doing optimization in a way like the central nerve systems of advanced animals including humans is promising from some viewpoints.

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