Computational Neuroscience Crash Course 2021

Given the increasing complexity of neural data and the generalized use of theoretical models in neuroscience, more and more neuroscientists rely on computationnal tools for modelling or data analysis. We would like to offer the possibility to those who feel that their maths / informatics background is a bit short to update their maths and to get familiar with basic techniques for data analysis/modelling using the Python language. The Computational Neuroscience Crash Course (CNCC) will span over two years, with a first part focusing on the maths and programming pre-requisites, and a second part next year on data analysis (and possibly modelling to follow).

For all courses (maths and programming), we’ll provide some theoretical background, propose small exercises for participant to work on their own and then solve the exercises together and make sure everybody has acquired the related concepts and techniques. Courses will be taught in English.

Aims:
1. To develop mathematical and numerical computing skills required for data analysis and modelling in neuroscience and biology
2. To create an exemplary code (Jupiter notebook) for basic data analysis
3. To be able to program a data analysis pipeline such as to automatically process batches of data files
4. To develop interaction between students and researchers.

Programme:
1. Mathematical prerequisites (linear algebra and signal processing)
2. Introduction to Python programming and numerical computing
3. Mini-project: analysis of songbird early vocalizations audio recordings (processing, visualization, discrimination/categorization.

Teaching method:
1. Pair programming for the mini-project
2. Lecture for maths pre-requisites and introduction to programming
3. For the remote training, a zoom link will be sent to the students.

Duration:
One week: 5 days of 2x3h: 30h

Deadline for registration: 15/06/2021

Date: 5/07/2021 – 9/07/2021

Timetable: https://github.com/rougier/CNCC-2021

Venue: Remote (due to covid 19)

Contact: