兰州理论物理中心学术报告——周昌松 教授

日期: 2023-01-05 阅读: 来源: 关键词:

主讲人:周昌松教授(香港浸会大学)

题目:Cost-Efficient Neural Dynamics: Reconciling Multilevel Spontaneous and Evoked Activity in E-I Balanced Neural Networks at Criticality(神经网络的临界性:统一复杂神经动力学多重特征及其成本效益)

时间:2023年01月12日下午14:30

会议ID:(腾讯会议)707-886-610

联系人:俞连春

报告摘要:

The brain is highly energy consuming, therefore is under strong selective pressure to achieve cost-efficiency in both cortical connectivity and activity. Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance. It is not clear how cost-efficiency is related to ubiquitously observed multi-level properties of irregular firing, oscillations and neuronal avalanches. In this talk, I will introduce a series of our work demonstrating that prominent multilevel neural dynamics properties can be simultaneously reconciled in a generic, biologically plausible neural circuit model that captures excitation-inhibition balance and realistic dynamics of synaptic conductance. Their co-emergence achieves minimal energy cost as well as maximal energy efficiency on information capacity, when neuronal firings are maintained in the form of critical neuronal avalanches. We propose a semi-analytical mean-field theory to derive the field equations governing the network macroscopic dynamics. It reveals that the critical state E-I balanced state of the network manifesting irregular individual spiking is characterized by a macroscopic stable state, which can be either a fixed point or a periodic motion and the transition is predicted by a Hopf bifurcation in the macroscopic field. An analysis of the impact of network topology from random to modular networks shows that local dense connectivity under E-I balanced dynamics appears to be the key“less-is-more”solutions to achieve cost-efficiency organization in neural systems. In the presence of external stimuli, the model at criticality can simultaneously account for various reliable neural response features observed in experiments. The modeling framework offers opportunities for study complex neural dynamics in neural information processing, as well as in cost-efficient brain-inspired artificial intelligence. I will show examples of studying learning, memory impairment and rescue using such E-I network with biological plasticity.

个人简介:

周昌松,物理学博士,香港浸会大学物理系教授、系主任,浸会大学非线性研究中心主任,计算及理论研究所副所长。1992年获南开大学物理学士, 1997年获南开大学物理博士,1997-2007年在新加坡、 香港、 德国等地从事访问研究,是洪堡基金获得者。2007年加入香港浸会大学物理系,2011年获浸会大学“杰出青年研究者校长奖”,2021年获“杰出研究者校长奖”。周昌松博士致力于复杂系统动力学基础研究及其应用,特别是网络的复杂联结结构与体系的动态行为的关系和相互作用。近几年与国际国内系统和认知神经科学家合作,把这些理论进展应用到大脑的复杂联结结构和活动以及认知功能及障碍的分析和建模等方面研究中。周昌松博士对生物神经网络复杂结构、动力学及其高成本效益如何启发类脑智能具有浓厚的兴趣。在国际交叉学术刊物PNAS,PRL,Nature Communications, Physics Reports,National Science Review, Cell Reports, Journal of Neuroscience, NeuroImage, PLoS Computational Biology等发表论文150余篇(Google Scholar引用16800余次,H-因子为48)。任Scientific Reports编委,PLoS One,Cognitive Neurodynamics学术编辑,及多种国际期刊常任审稿人。

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