Neuromorphic Cognitive Systems - Yu, Qiang

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Résumé :
This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed. The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail.

Biographie:
Qiang Yu received his Ph.D. degree from University of Bundeswehr Muenchen, Munich, Germany in 2012. From 2008-2012 he was an engineer in FEAAM GmbH, Neubiberg, Germany, where he hosted the project design and analysis of high efficient canned switched reluctance machine drives for hydraulic pump drives, with KSB Aktiengesellschaft, Frankental, Germany. From 2013-2014 he was a postdoctoral research associate at Automotive Resource Center, McMaster University, Ontario, Canada, where he hosted the project high efficient rare-earth free machine drives. From 2014-2015 he was a postdoctoral research fellow in Universite Libre de Bruxelles, Brussels, Belgium, with a European funded project DeMoTest EV (Design, Modeling and Test of Electrical Vehicles). Currently he is an associate professor in School of Electrical and Power Engineering, China University of Mining and Technology. His main research interests include electromagnetic and thermal analysis of electrical machines, cannedmachine drives and mathematical modeling of electrical machines. Xuesong Wang received her Ph.D. degree from China University of Mining and Technology in 2002. She is currently a professor in School of Information and Control Engineering, China University of Mining and Technology. Her main research interest includes electrical drives, bioinformatics, and artificial intelligence. In 2008, she was the recipient of the New Century Excellent Talents in University from the Ministry of Education of China. Yuhu Cheng received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. He is currently a professor in School of Information and Control Engineering, China University of Mining and Technology. His main research interest includes electrical drives and intelligent systems. In 2010, he was the recipient of the New Century Excellent Talents in University from the Ministry of Education of China. Lisi Tian received his Ph.D. degree from Huazhong University of Science and Technology (HUST), China in 2015. He is currently with the School of Electrical and Power Engineering, China University of Mining and Technology. His main research interests include power electronics, electrical drives and fault diagnosis....

Sommaire:
 Introduction.-  Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons.-  A Spike-Timing Based Integrated Model for Pattern Recognition.-  Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns.- A Spiking Neural Network System for Robust Sequence Recognition.- Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections.- A Hierarchically Organized Memory Model with Temporal Population Coding.- Spiking Neuron Based Cognitive Memory Model.