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.

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Important dates

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 ([email protected] or [email protected]). We'll
send email when new material is online and you can ask us for help if you
encounter some diffculties.

Mathematical lessons

Programming lessons

Project

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Mathematical lessons

Linear Algebra

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.

Prerequisites: None
See also:
Wikipedia | Linear Algebra
& Mathematics for Computational Neuroscience

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Differential Equations

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.

Prerequisites: None
See also:
Wikipedia | Differential Equations &
Mathematics for Computational Neuroscience

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Signal Processing

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.

Prerequisites: None
See also:
MATLAB for Neuroscientists ($)

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Programming lessons

Installation

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.

Prerequisites: None
See also: Anaconda installation
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Introduction

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.

Prerequisites: Installation
See also: Official Python tutorial & Dive into Python
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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.

Prerequisites: Introduction
See also: Scipy Lecture Notes, Numpy for Matlab users & From Python to Numpy
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Data Visualization

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.

Prerequisites: Numerical computing
See also: Matplotlib tutorial, Ten Simple Rules for Better Figures

Scientific computing

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.

Prerequisites: Numerical computing
See also: Elegant Scipy, Python Data Science Handbook

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Project

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.

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Computational Neuroscience Crash Course (CNCC 2019)
Copyright © 2019 Arthur Leblois & Nicolas P. Rougier – CC-BY 4.0 International license.