Computational Neuroscience and Inference from data are disciplines that extensively use tools from mathematics and physics to understand the behavior of model neuronal networks and analyze data from real experiments. Due to its interdisciplinary nature and the complexity of the neuronal networks, the list of techniques that are borrowed from Physics and Mathematics is an extensive one. Although using tools from standard curriculum of Physics, Mathematics and Engineering is common, more advanced research requires methods and techniques that are not usually covered in any single discipline.
To fill in this gap, this 4 week summer school covers some of the most important methods used in computational neuroscience research through both main lectures and scientific seminars (5-6 main lectures per topic and 1-2 seminars by each invited seminar speaker) .
Organizers: Yasser Roudi and Benjamin Dunn
Main Lectures and Lecturers
Information Theory and Inference
Matto Marsili, ICTP
Statistical Mechanics of Neural Networks
Yasser Roudi & Nicola Bulso, KISN
Kamiar Rahnama Rad, CUNY
Dynamics of Neural Networks
John Hertz, Nordita
Invited seminars speakers (this is not a final list and might change)
Moritz Helias, Research Centre Jülich, Germany
Simona Cocco, ENS, France
Juan Gallego, Imperial College, UK
Arvind Kumar, KTH, Sweden
Remi Monasson, ENS, France
Alex Roxin, Centre de Rece. Mat., Spain
Gaute Einevoll, Univ. of Oslo, Norway
Bahador Bahrami, LMU, Germany
Peter Latham, UCL, UK
Barry Richmond, NIH, US
Alessandro Treves, SISSA, Italy
The deadline for applications is 15 May 2020.
Travel, accommodation and living costs for participants who are members of NRSN will be covered by NRSN. A limited amount of financial help is also available for other participants.
About Kavli Moen Gård
Kavli Moen Gård is the family farm where Fred Kavli grow up. It is located in the scenic area of Esfjord and is a gathering place for programs that stimulate curiosity, innovation, and big ideas.
For further information see the website of Kavli Moen Gård