## 2017-03-15 Reproducing Olshausens classical SparseNet (part 2)

• In a previous notebook, we tried to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.

• However, the dictionaries are qualitatively not the same as the one from the original paper, and this is certainly due to the lack of control in the competition during the learning phase.

• Herein, we re-implement the cooperation mechanism in the dictionary learning routine - this will be then proposed to the main code.

This is joint work with Victor Boutin.

## 2017-03-14 Reproducing Olshausen's classical SparseNet (part 1)

• This notebook tries to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.

• the underlying machinery uses a similar dictionary learning as used in the image denoising example from sklearn and our aim here is to show that a novel ingredient is necessary to reproduce Olshausen's results.

• All these code bits is regrouped in the SHL scripts repository (where you will also find some older matlab code). You may install it using

pip install git+https://github.com/bicv/SHL_scripts

## basics of probability theory¶

In the context of a course in Computational Neuroscience, I am teaching a basic introduction in Probabilities, Bayes and the Free-energy principle.

Let's learn to use probabilities in practice by generating some "synthetic data", that is by using the computer's number generator.

## 2017-01-15 Bogacz (2017) A tutorial on free-energy

I enjoyed reading "A tutorial on the free-energy framework for modelling perception and learning" by Rafal Bogacz, which is freely available here. In particular, the author encourages to replicate the results in the paper. He is himself giving solutions in matlab, so I had to do the same in python all within a notebook...

## generating databases¶

A set of bash code to resize images to a fixed size.

Problem statement: we have a set of images with heterogeneous sizes and we want to homogenize the database to avoid problems when classifying them. Solution: ImageMagick.

We first identify the size and type of images in the database. The database is a collection of folders containing each a collection of files. We thus do a nested recursive loop:

## 2016-11-24 Using generators in Python

Let's explore generators and the yield statement in the python language...

## 2016-11-17 Finding extremal values in a nd-array

Sometimes, you need to pick up the $N$-th extremal values in a mutli-dimensional matrix.

Let's suppose it is represented as a nd-array (here, I further suppose you are using the numpy library from the python language). Finding extremal values is easy with argsort but this function operated on 1d vectors... Juggling around indices is sometimes not such an easy task, but luckily, we have the unravel_index function.

Let's unwrap an easy solution combining these functions:

## 2016-11-15 Saving and displaying movies and dynamic figures

It is insanely useful to create movies to illustrate a talk, blog post or just to include in a notebook:

In [1]:
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="../files/noise.mp4" width=61.8%/></center>')
Out[1]:

For years I have used a custom made solution made around saving single frames and then calling ffmpeg to save that files to a movie file. That function (called anim_save had to be maintained accross different libraries to reflect new needs (going to WEBM and MP4 formats for instance). That made the code longer than necessary and had not its place in a scientific library.

Here, I show how to use the animation library from matplotlib to replace that