Academic Programs

CNEURO 2020: Theoretical and Computational Neuroscience Summer School


About CNeuro

How intelligence and behavior emerge from complex and intricate interactions within the brain remains a deep and unsolved mystery, central to an exciting area of interdisciplinary research. The past decade has seen rapid progress in experimental tools that now make it possible to monitor and manipulate brain circuits in unprecedented detail. This evolution presents challenges and opportunities for both experimentalists and theorists, as the complex algorithmics of brain function and the intricate interactions among neurons cannot be approached with experiments alone. Mathematical theory is instrumental in the emergence of theoretical insights and frameworks that can help guide experimental work and identify unifying principles of brain function.

The aim of the one-week summer school will be to introduce students with a strong quantitative background (in mathematics, physics, computer science, and engineering) to the emerging field of theoretical and computational neuroscience. The course will bring together leading scientists in the field, who will deliver lectures, take part in small-group discussions, and share their personal experience and views on a range of research topics. The distinguishing feature of CNeuro is the emphasis it places on the role of systematic mathematical theory for understanding the brain, in part by stressing the connections between neuroscience, statistics, machine learning, and artificial intelligence. The summer school will serve as a pedagogical introduction to some of the methods particularly relevant to exploring these connections.This year will be the third installment of the CNeuro summer school. We will make every effort to recruit students from a diverse background including all genders and ethnic groups. 

Course Structure and Curriculum

Each day will include several hours of lectures as well as two hours of discussions among students and faculty, in small groups. Topics will touch upon the biophysics and dynamics of neurons and network, neural coding, models of learning and other cognitive function, as well as machine learning and Bayesian approaches. There will be the possibility for students to work on problem sets and initiate independent projects.


Rava Azeredo da Silveira (ENS) 
Stella Christie (Tsinghua University)
Damon Clark (Yale University) 
Carina Curto (Penn State University)
Ralf Haefner (University of Rochester)
Yu Hu (HKUST) 
Daniel Lee (Cornell University)
Songting Li (Shanghai JiaoTong University)
Zhaoping Li (MPI of Biologial Cybernetics) 
Cristina Savin (NYU)
Eric Shea-Brown (University of Washington)
Sara Solla (Northwestern University)
Sen Song (Tsinghua University)
Louis Tao (Peking University)
Xiaoqin Wang (Johns Hopkins University)
Quan Wen (USTC)
Hang Zhang (Peking University)
Kechen Zhang (Johns Hopkins University)
Douglas Zhou (Shanghai JiaoTong University)

Apply by July 15th, 2020 

Please submit CV and personal statement to

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