Plenary Lecture 8

Jul. 31St  15:30 – 16:30

Prof. Qiang  Yu

Tianjin University, China

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Title: Neuromorphic Approaches for Environmental Sound Recognition

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The capability for environmental sound recognition (ESR) can determine the fitness of individuals in a way to avoid dangers or pursue opportunities when critical sound events occur. It still remains mysterious about the fundamental principles of biological systems that result in such a remarkable ability. Additionally, the practical importance of ESR has attracted an increasing amount of research attention, but the chaotic and non-stationary difficulties continue to make it a challenging task. In this talk, I will present a spike-based framework from a more brain-like perspective for the ESR task. The framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout. I will show that the spike-based framework has several advantageous characteristics including early decision making, small dataset acquiring and ongoing dynamic processing. The outstanding performance of the introduced framework would potentially contribute to draw more research efforts to push the boundaries of spike-based paradigm to a new horizon.

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BIO

Qiang YU received the B.Eng. degree in electrical engineering and automation from the Harbin Institute of Technology, Harbin, China, in 2010, and the Ph.D. degree in electrical and computer engineering from the National University of Singapore, Singapore, in 2014. He is an Associate Professor with the College of Intelligence and Computing, Tianjin University, Tianjin, China. He is a recipient of the 2016 IEEE Outstanding TNNLS Paper Award.

He works with information processing and cognitive computation in spiking neuronal networks trying to reveal and utilize mechanisms how biological systems give rise to various cognitive abilities. His main research topics include spike-based learning in both single and multilayer neural networks, sensory coding, cognitive memory, neural circuits, system modeling, as well as applied development of visual and speech processing systems.

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