潘纲

浙江大学计算机学院教授

个人介绍：

浙江大学计算机学院教授、博导，计算机辅助设计与图形学国家重点实验室副主任，中国人工智能学会常务理事、脑机融合与生物机器智能专委会主任委员，中国计算机学会普适计算专委会常务委员。主要研究方向为混合智能、脑机接口、类脑计算、计算机视觉、普适计算等。获CCF-IEEE CS青年科学家奖、CCF-A类会议最佳论文奖1次、CCF-A类会议最佳论文提名奖2次。获国家科学技术进步奖二等奖（第2完成人）、教育部科技进步一等奖（第2完成人）。

Gang Pan a professor of the Department of Computer Science, and deputy director of State Key Lab of CAD&CG, Zhejiang University, China. His current interests include artificial intelligence, brain-inspired computing, brain-machine interfaces, pervasive computing, and computer vision. He has authored over 100 refereed papers, and 35 patents granted. Dr. Pan received three best paper awards and three nominations from premier international conferences. He is the recipient of IEEE TCSC Award for Excellence (Middle Career Researcher), CCF-IEEE CS Young Computer Scientist Award, and National Science and Technology Progress Award. He serves as an Associate Editor of IEEE Trans. Neural Networks and Learning Systems (2019.1-), IEEE Systems Journal, Pervasive and Mobile Computing, and ACM Proceedings of Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).

议题：

脉冲神经网络:模型与应用

Spiking Neural Networks: Models and Applications

议题介绍：

脉冲神经网络（Spiking Neural Networks）由于比传统的人工神经网络具有更好的生物逼真性，近年来受到研究人员越来越多的关注。通过脉冲神经网络，计算系统与生物神经系统的连接融合有望变得更加有效与自然。本报告将介绍脉冲神经网络原理与方法，以及若干脉冲神经网络的典型应用。同时，也将分享课题组近年在脉冲神经网络方面的研究进展。

Recently, spiking neural networks (SNN) has received significant attentions for its biological plausibility. SNN theoretically has at least the same computational power as traditional artificial neural networks (ANNs), and it has the potential to achieve revolutionary energy-efficiency. This talk will introduce models and methods of spiking neural networks, some typical applications, as well as our recent work on SNNs.