NeuroNex Theory Team: Columbia University Theoretical Neuroscience Center
NeuroNex Theory Team: Columbia University Theoretical Neuroscience Center
批准号:
1707398
负责人:
Laurence Abbott
金额:
$304.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-08-01 至 2023-07-31
中文摘要
了解健康的大脑是如何解释感觉信号和指导行动的,以及为什么不健康的大脑无法正确地执行这些功能,这是21世纪科学的一个深远而雄心勃勃的目标。将神经回路功能的知识整合到感知、认知和行动的连贯图景中,需要三个研究领域之间的非凡合作和协调:实验、数据分析和建模。哥伦比亚大学的国家科学基金会理论团队将联合统计数据分析和理论建模方面的特殊资源,与广泛的实验合作者网络合作,以应对神经科学面临的巨大挑战。对理论见解和复杂数据分析的需求从未像现在这样迫切。神经科学领域正面临着来自一个系统的复杂数据洪流,而这个系统本身就非常复杂。未来的进展需要发展通过分析和建模从这些数据中提取知识和理解的能力,以捕捉它们的含义的本质。哥伦比亚大学的NeuroNex理论团队的目标是通过其研究的质量、受训人员的卓越以及访客、传播和推广计划的影响,建立一种新的合作范式,将神经科学推向前所未有的发现和理解水平。高密度电极记录、广域钙成像和复杂的连接映射正在将神经科学带入一个广泛的多领域甚至全脑研究神经活动和电路的时代。神经科学界迫切需要新的方法来解释从不同物种获得的数据,使用无数的技术,并考虑大长度、大时间尺度和跨多个大脑区域的神经处理。为了应对这些挑战,两个主要目标将推动和定义哥伦比亚大学神经网络理论团队的研究:第一,将用于从数据中推断含义的分析方法和理论模型相互整合,并与产生这些数据的实验相结合;第二,提供分析工具和理论框架,以了解多个大脑区域之间的相互作用,并从利用不同物种的各种技术的实验中吸取重要的总体经验教训。通过将理论技术与致力于各种系统和物种的杰出实验合作者紧密结合起来,将取得进展。研究生和博士后培训将强调在理论和实验神经科学方面的技术卓越和广阔的前景。将通过访客和交流方案、赞助会议和传播研究成果以及高质量、方便用户的软件,与其他研究人员进行接触。将通过与中小学生和高中生以及普通公众分享神经科学研究的兴奋来扩大到更广泛的社区。这个NeuroNex理论团队奖是由生物科学局的新兴前沿司、数学和物理科学局的物理司和数学司以及社会、行为和经济科学局的脑和认知科学司共同资助的,作为大脑倡议和NSF了解大脑活动的一部分。
英文摘要
Understanding how a healthy brain interprets sensory signals and guides actions, and why an unhealthy brain fails to perform these functions properly, is a profound and ambitious goal of 21st century science. Integrating knowledge of neural circuit function into a coherent picture of perception, cognition and action requires extraordinary cooperation and coordination between three research areas: experimentation, data analysis and modeling. The National Science Foundation Theory Team at Columbia University will unite exceptional resources in statistical data analysis and theoretical modeling with an extensive network of experimental collaborators to address the enormous challenges facing neuroscience. Never has the need been greater for theoretical insights and sophisticated data analysis. The field of neuroscience is facing a torrent of complex data from a system that is, itself, extraordinarily complex. Future progress requires developing the ability to extract knowledge and understanding from these data through analyses and modeling that capture the essence of what they mean. The goal of the NeuroNex Theory Team at Columbia is to establish, through the quality of its research, the excellence of its trainees, and the impact of its visitor, dissemination, and outreach programs, a new cooperative paradigm that will move neuroscience to unprecedented levels of discovery and understanding.High-density electrode recording, wide-field calcium imaging and complex connectivity mapping are bringing neuroscience into an era of extensive multi-area and even whole-brain studies of neural activity and circuitry. The neuroscience community desperately needs new ways of interpreting data obtained from different species using myriad techniques and for thinking about neural processing over large length and time scales and across multiple brain areas. In response to these challenges, two major goals will drive and define research at the NeuroNex Theory Team at Columbia: first, integrating the analysis methods and theoretical models used to infer meaning from data with each other and with the experiments that generate these data; and second, providing analytic tools and theoretical frameworks to understand interactions between multiple brain regions and to draw important overarching lessons from experiments exploiting a variety of techniques across different species. Progress will be made through a tight integration of theoretical techniques with outstanding experimental collaborators working on a variety of systems and species. Graduate and postdoctoral training will stress technical excellence and broad perspectives in both theoretical and experimental neuroscience. Outreach will be made to other researchers through visitor and exchange programs, sponsored meetings and dissemination of research results and high-quality, user-friendly software. Outreach will be made to the broader community by sharing the excitement of neuroscience research with elementary and high school students and with the general public. This NeuroNex Theory Team award is co-funded by the Division of Emerging Frontiers within the Directorate for Biological Sciences, the Division of Physics and the Division of Mathematics within the Directorate of Mathematical and Physical Sciences, and by the Division of Brain and Cognitive Sciences within the Directorate of Social, Behavioral and Economic Sciences, as part of the BRAIN Initiative and NSF's Understanding the Brain activities.
期刊论文(136)
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科研奖励(0)
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DOI:
--
发表时间:
2018-11
期刊:
影响因子:
--
作者:
[Daniel Hernandez;A. Moretti;S. Saxena;Ziqiang Wei;J. Cunningham;L. Paninski]
通讯作者:
Daniel Hernandez;A. Moretti;S. Saxena;Ziqiang Wei;J. Cunningham;L. Paninski
Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models?
生物学真实的循环架构是否能产生生物学真实的模型?
DOI:
10.32470/ccn.2019.1175-0
发表时间:
2019
期刊:
2019 Conference on Cognitive Computational Neuroscience
影响因子:
--
作者:
[Lindsay, Grace, Moskovitz, Theodore, Yang, Guangyu Robert, Miller, Kenneth]
通讯作者:
Miller, Kenneth
DOI:
10.7554/elife.67620
发表时间:
2022-05-27
期刊:
ELIFE
影响因子:
7.7
作者:
[Saxena, Shreya, Russo, Abigail A., Cunningham, John, Churchland, Mark M.]
通讯作者:
Churchland, Mark M.
DOI:
10.1016/j.cub.2021.09.061
发表时间:
2021-12-06
期刊:
Current biology : CB
影响因子:
--
作者:
[Kohn JR, Portes JP, Christenson MP, Abbott LF, Behnia R]
通讯作者:
Behnia R
DOI:
10.1016/j.neuron.2022.06.018
发表时间:
2022-09-07
期刊:
NEURON
影响因子:
16.2
作者:
[Abe, Taiga, Kinsella, Ian, Saxena, Shreya, Buchanan, E. Kelly, Couto, Joao, Briggs, John, Kitt, Sian Lee, Glassman, Ryan, Zhou, John, Paninski, Liam, Cunningham, John P.]
通讯作者:
Cunningham, John P.
共 65 条
Mathematical Modeling of Neural Populations
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批准号:0748976
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项目类别:Continuing Grant
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资助金额:$10.19万
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财政年份:2007
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负责人:Laurence Abbott
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依托单位:
Mathematical Modeling of Neural Populations
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批准号:0235463
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项目类别:Continuing Grant
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资助金额:$51.79万
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财政年份:2003
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负责人:Laurence Abbott
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依托单位:
Mathematical Modeling of Neural Populations
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批准号:9817194
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项目类别:Standard Grant
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资助金额:$26.4万
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财政年份:1999
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负责人:Laurence Abbott
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依托单位:
Decoding Methods for Predicting Postsynaptic Responses to Spike Trains
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批准号:9421388
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项目类别:Continuing Grant
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资助金额:$26.05万
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财政年份:1995
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负责人:Laurence Abbott
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依托单位:
Mathematical Sciences:Mathematical Modeling of Neural Populations
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批准号:9503261
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项目类别:Standard Grant
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资助金额:$16.0万
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财政年份:1995
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负责人:Laurence Abbott
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依托单位:
Development of the Dynamic Clamp
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批准号:9312975
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项目类别:Standard Grant
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资助金额:$5.0万
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财政年份:1993
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负责人:Laurence Abbott
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依托单位:
Mathematical Sciences: Modeling of Neural Populations
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批准号:9208206
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项目类别:Continuing Grant
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资助金额:$15.7万
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财政年份:1992
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负责人:Laurence Abbott
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依托单位:
国内基金
海外基金
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批准号:24ZR1403900
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资助金额:55万元
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英文专著《FRACTIONAL INTEGRALS AND DERIVATIVES: Theory and Applications》的翻译
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基于Restriction-Centered Theory的自然语言模糊语义理论研究及应用
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批准号:61671064
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项目类别:面上项目
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资助金额:65.0万元
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批准年份:2016
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负责人:史树敏
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