# 2017-03-29 testing COMPs-fastPcum_scripted

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). Compared to the previous post, we integrated the faster code to https://github.com/bicv/SHL_scripts.

This is joint work with Victor Boutin.

# 2017-03-29 testing COMPs-fastPcum

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). Compared to the previous post, we optimize the code to be faster.

This is joint work with Victor Boutin.

# 2017-03-29 testing COMPs-Pcum

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). In particular, we will show how one can build the non-linear functions based on the activity of each filter and which implement homeostasis.

This is joint work with Victor Boutin.

# 2015-05-22 A hitchhiker guide to Matching Pursuit

The Matching Pursuit algorithm is popular in signal processing and applies well to digital images.

I have contributed a python implementation and we will show here how we may use that for extracting a sparse set of edges from an image.

bibtex
@inbook{Perrinet15bicv,
title = {Sparse models},
author = {Perrinet, Laurent U.},
booktitle = {Biologically-inspired Computer Vision},
chapter = {13},
citeulike-article-id = {13566753},
editor = {Keil, Matthias and Crist\'{o}bal, Gabriel and Perrinet, Laurent U.},
publisher = {Wiley, New-York},
year = {2015}
}