Computational Neuroscience Crash Course (2019)
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 (2019) focusing on the maths and programming pre-requisites, and a second part next year (2020) on data analysis (and possibly modelling to follow).
The course is free and open to everyone (student, post-docs, researchers...) but we'll give priority to master and PhD students given the limited number of places (n=24). For all courses (maths and programming), we'll provide some theoretical background, propose small exercices for participant to work on their own and then solve the exercices together and make sure everybody has acquired the related concepts and techniques. Courses will be taught in English.
Registration deadline: 31/03/2019 (registrations are now CLOSED)
If you were not able to register but want to be kept up to date, you can send us an email (firstname.lastname@example.org or email@example.com). We'll send email when new material is online and you can ask us for help if you encounter some diffculties.
|April 5, 2019||9:30-11:30||ED Building, room 30||Linear Algebra||Arthur Leblois|
|April 12, 2019||9:30-11:30||ED Building, room 30||Differential Equations||Arthur Leblois|
|April 26, 2019||9:30-11:30||ED Building, room 30||Signal Processing||Arthur Leblois|
|July 1, 2019||9:00-12:00||ED Building, room 30||Introduction||Nicolas Rougier|
|14:00-17:00||ED Building, room 30||Numerical Computing||Nicolas Rougier|
|July 2, 2019||9:00-12:00||ED Building, room 30||Data Visualization||Nicolas Rougier|
|14:00-17:00||ED Building, room 30||Scientific Computing||Nicolas Rougier|
|July 3, 2019||9:00-12:00||ED Building, room 30||Presentation||Nicolas Rougier & Arthur Leblois|
|14:00-17:00||ED Building, room 30||Team work (part 1)||-|
|July 4, 2019||9:00-12:00||ED Building, room 30||Update||Nicolas Rougier & Arthur Leblois|
|14:00-17:00||ED Building, room 30||Team work (part 2)||-|
|July 5, 2019||9:00-12:00||ED Building, room 30||Update||Nicolas Rougier & Arthur Leblois|
|14:00-16:00||ED Building, room 30||Wrap-up||Nicolas Rougier & Arthur Leblois|
This course will introduce vectors and matrices, how to peform operations such as addition & multiplication on these objects. The correspondence with geometry and the resolution of a system of linear equations will be explained.
We'll cover first-order differential equations (that can for example describe the evolution of a membrane potential). We'll see how to analyze and solve such equation.
We'll explain first what is the Fourier transform that is ubiquituous in signal processing, what is spectral analysis and how to compute correlation in order to reveal similarity between signals.
See also: MATLAB for Neuroscientists ($)
This lesson aims at providing the student with a clean development environment, including Python installation and essential packages, a decent text editor, and a shell. We'll also introduce the Python & IPython shells, the Jupyter notebook and explains how to run a python script from the command line.
See also: Anaconda installation
We introduce here Python, a powerful and easy to learn programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming (Python website). However, we'll only cover the strict minimum necessary for getting started with numerical computing.
This lesson gives an overview of NumPy, the core library for performant numerical computing, with support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
We'll explore the matplotlib library which is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.
This lesson, from the scipy mlecture notes, will cover scipy which is a scientific-computing libraries, such as the GSL (GNU Scientific Library for C and C++), or Matlab’s toolboxes. Scipy is the core package for scientific routines in Python; it is meant to operate efficiently on numpy arrays, so that numpy and scipy work hand in hand.
The goal of the project is to sort (automatically) audio files that correspond to the recording of adult or juvenile songbirds. If you listen to the audio files, you will hear that the sound is quite different between an adult (song) and a juvenile (babbling). This means we can probably process the audio files in order to decide if it corresponds to an adult or a juvenile and the goal is thus to write a function songsort("./some-path/") that will automatically sort all the files present in some-path and label them accordingly.
For the project, you'll need to team with someone else sucha as to work together at one compputer (pair programming). When one is typing, the other is reading an commenting and for maximum efficiency, you'll have to switch roles frequently.
Computational Neuroscience Crash Course (CNCC 2019)
Copyright © 2019 Arthur Leblois & Nicolas P. Rougier – CC-BY 4.0 International license.