# "An adaptive algorithm for unsupervised learning"¶

This supplementary information presents :

• first, the code to generate the figures from the paper,
• second, some control experiments that were mentionned in the paper,
• finally, some perspectives for future work inspired by the algorithms presented in the paper.

A convenience script model.py allows to run and cache most learning items in this notebooks:

## figure 1: Role of homeostasis in learning sparse representations¶

### learning¶

The actual learning is done in a second object (here dico) from which we can access another set of properties and functions (see the shl_learn.py script):