报告人简介

李远宁，上海科技大学生物医学工程学院研究员、助理教授、博导、独立课题组组长，美国卡内基梅隆大学神经计算与机器学习博士，加州大学旧金山分校神经外科博士后，长期从事计算认知神经科学研究，入选国家高层次青年人才计划，曾获美国国立卫生研究院“神经科学杰出学者奖”，2023年“脑科学与类脑智能科创新青年30人”，第一及通讯作者成果发表在Nature Neuroscience,Nature Communications,Science Advances,PNAS等期刊，参与科技创新2030 “脑科学与类脑研究”重大项目等科研项目。

内容简介

Understanding the shared coding of speech and language between deep neural network models and the human brain   Speech and language play crucial roles in human communication, cognition, and social interactions. This talk explores the convergence between deep neural network (DNN) models and the human auditory and language processing systems. By analyzing the neural coding from the auditory nerve to the speech cortex using state-of-the-art DNN representations, we found that self-supervised learning (SSL) models, such as Wav2Vec2 for speech and GPT-2 for language, demonstrated high prediction accuracy of neural responses. Shared components between speech and language DNNs suggest that contextual and acoustic-phonetic information encoded in these models contribute to distinct spatiotemporal dynamics in brain activity. These findings reveal a significant alignment between DNN model representations and the neural processes underlying speech and language, offering novel insights into the modeling of neural coding in the auditory and language networks.