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Neuronal Diversity in Deep Learning

日期:2023-10-30                   来源:                   作者:               关注:

报告题目: Neuronal Diversity in Deep Learning




Deep learning, represented by deep artificial neural networks, has been dominating numerous important research fields in the past decade. Although the invention of the neural network was to mimic a human's brain, the current development of deep learning is not primarily driven by the increasingly growing understanding to the brain. Brain is the most intelligent system we have ever known so far, although the brain remains vastly undiscovered, it is clear that the existing deep learning still goes far behind human brain in many important aspects such as efficiency, interpretability, memory, etc. Given the incredible capability of the human brain, we argue that neuroscience can always offer support for deep learning as a think tank and a validation means. Clearly, the characters of the current mainstream deep learning models are fundamentally different from the biological neural system. One remarkable distinction is that the deep learning models lack the neuronal diversity that is everywhere in the human brain. Different from artificial networks that are built on a single universal primitive neuron type, the human brain has numerous morphologically and functionally diverse neurons. The neuronal diversity is an enabling factor for all kinds of intelligent behaviors. In this talk, I will discuss what values can the neuronal diversity potentially add to the artificial neural network.


范凤磊男,博士,香港中文大学数学系研究助理教授,研究方向为深度学习理论,建模与应用。本科毕业于哈尔滨工业大学,博士毕业于美国伦斯勒理工BOB(Rensselaer Polytechnic Institute),团队导师为国际知名影像专家王革教授, 随后在美国康乃尔大学完成为期一年的博士后研究。发表论文20余篇,主要研究成果发表于人工智能领域和图像处理领域的旗舰杂志如JMLR, IEEE TNNLS, IEEE TMI, IEEE TCI, IEEE TAI, 代表成果为基于二阶神经元的深度学习体系和神经网络宽度深度对称性。据 Google Scholar 统计,成果被麦克阿瑟天才奖得主兼加州理工大学教授Colin CamererACM 会士C.-C. Jay Kuo、美国工程院院士Charbel FarhatIEEE会士Michael Unser等人引用800余次。在人工智能顶级会议AAAI2023组织tutorial,获得广泛关注和好评,并受邀担任中科院二区杂志Frontiers in Human Neuroscience 特刊“ Brain Imaging, Stimulation, and Analysis”的客座编辑。攻读博士学位期间获得IBM AI Horizon Scholarship的资助(共计20w美金),并受邀前往麻省理工BOB-IBM人工智能实验室实习。博士论文被国际神经网络协会(INNS)授予2021年博士论文奖(每年仅授予一名)。