学术报告报告题目：Human brain energetic connectome based on neuropil distribution
报 告 人：于玉国 教授（复旦大学）
报告时间：2020年11月22日（星期日）上午09：00-10：00
报告地点：4号楼318室
邀 请 人：刘深泉 教授
欢迎广大师生前往！
数学学院
2020年11月16日
报告摘要：
Abstract: Human cognition emerges from high metabolic energy demands of the brain, which impose limits on how much energy is allocated for functional and structural needs. Yet, how local variations in cellular and synaptic constituents of cortical networks affect regional variations in cerebral metabolism remains unknown. Based on principles that neuronal-glial glucose oxidation fuels signaling and nonsignaling activities of the neuropil, we created a high-resolution three-dimensional (3D) model of the human brain energetic connectome, based on blueprints of cellular and synaptic densities. Transcortical gradients of cellular/synaptic densities, cortical energetics, and neural firing revealed complex structure-function relationships. Strong correlation between glucose oxidation and synapse density maps suggests that firing patterns are more based on both local and distal connectivity than cellular mass distributions. The energy connectome in millimeter scale provides a basic framework to understand energy constrained cortical circuits across a range of spatial scales mapped by functional imaging.
报告人简介：
于玉国，复旦大学生命科学学院教授，计算神经科学实验室负责人。南京大学凝聚态物理学博士(2001)，美国卡耐基梅隆大学计算神经科学博士后(2004)，耶鲁大学医学院副研究员(2010)。Frontiers in Computational Neuroscience和Cognitive Neurodynamics等杂志编委。应用物理学理论和数学模型，结合实验方法进行跨学科研究大脑脑皮层神经电活动规律和信息处理机制、脑认知计算方式和能量代谢机制。在Nature, PNAS, Neuron, Physical Review Letters, Journal of Neuroscience, PLoS Compuational Biology，EBioMedicine等SCI学术期刊发表论文50余篇，在神经信息处理和类脑计算机制方面的研究对发展人工智能技术具有参考价值。