Title: Introducing Neuronal Diversity into Deep Learning
Speaker: Fenglei Fan（The Chinese University of Hong Kong）
Time: Thursday, November 24, 2022. 16:00-17:30
Location: B201-1, Mingde Building
Tencent meeting , Meeting ID:260 305 862, Password:1124
Abstract: 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 add to the artificial neural network, as well as how this can shed light on engineering problems.
