Cours et tutoriels

Sun, 05 Apr 2020 00:00:00 +0000

Open Science

Wed, 27 Apr 2016 00:00:00 +0000

To enable the dissemination of the knowledge that is produced in our lab, we share all source code with open source licences. This includes code to reproduce results obtained in papers (e.g. (Perrinet, Adams and Friston, 2015), (Perrinet and Bednar, 2015), (Khoei et, 2017), (Perrinet, 2019), (Pasturel et al, 2020), (Dauce et al, 2020)) or courses and slides (e.g. 2019-04-03: vision and modelization, 2019-04-18_JNLF, …) and also the development of the following libraries on GitHub.

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HD natural images database for sparse coding

A dataset of natural images, acquired with a Canon EOS6D and Canon EOS650. It has been curated to facilitate research, namely in sparse coding at the moment, but can be used for future endeavors. Maintainer: Hugo Ladret.

get the dataset
See the preprint publication @
Hugo Ladret, Laurent U Perrinet (2023). Convolutional Sparse Coding is improved by heterogeneous uncertainty modeling. ICLR 2023 SNN Workshop.
PDF Cite URL

Bayesian Change Point

A python implementation of Adams & MacKay 2007 “Bayesian Online Changepoint Detection” for binary inputs in Python.

Source code
See the final publication @
Chloé Pasturel, Anna Montagnini, Laurent U Perrinet (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology.
Cite DOI Press Pdf Code URL bioRxiv

ANEMO: Quantitative tools for the ANalysis of Eye MOvements

This implementation proposes a set of robust fitting methods for the extraction of eye movements parameters.

Source code
See a poster @ Pasturel, Montagnini and Perrinet (2018)
This library was used in the following publication @
Chloé Pasturel, Anna Montagnini, Laurent U Perrinet (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology.
Cite DOI Press Pdf Code URL bioRxiv

LeCheapEyeTracker

Work-in-progress : an eye tracker based on webcams.

Source code

Biologically inspired computer vision ( Python)

SLIP: a Simple Library for Image Processing

This library collects different Image Processing tools for use with the LogGabor and SparseEdges libraries.

LogGabor: a Simple Library for Image Processing

This library defines the set of LogGabor kernels. These are generic edge-like filters at different scales, phases and orientations. The library develops a simple method to construct a simple multi-scale linear transform.

Web-site
Source code
This library is detailed in the following publication
Sylvain Fischer, Filip Šroubek, Laurent U Perrinet, Rafael Redondo, Gabriel Cristóbal (2007). Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision.
PDF Cite DOI Code URL
LogGabor filters are used in numerous computer vision applications and reaches 177 citations on Google Scholar (last updated 22/10/2021).

SparseEdges: sparse coding of natural images

Our goal here is to build practical algorithms of sparse coding for computer vision.

This class exploits the SLIP and LogGabor libraries to provide with a sparse representation of edges in images.

Web-site
Source code
This algorithm was presented in the following paper, which is available as a reprint
Laurent U Perrinet (2015). Sparse Models for Computer Vision. Biologically Inspired Computer Vision.
Cite DOI Code URL arXiv
It was notably used in the following paper
Laurent U Perrinet, James A Bednar (2015). Edge co-occurrences can account for rapid categorization of natural versus animal images. Scientific Reports.
Cite DOI Code URL HAL

Sparse Hebbian Learning : unsupervised learning of natural images

This is a collection of python scripts to test learning strategies to efficiently code natural image patches. This is here restricted to the framework of the SparseNet algorithm from Bruno Olshausen (http://redwood.berkeley.edu/bruno/sparsenet/).

Source code
This algorithm was presented in the following paper
Laurent U Perrinet (2010). Role of homeostasis in learning sparse representations. Neural Computation.
PDF Cite DOI Hal Code URL arXiv
54 citations on Google Scholar (last updated 22/10/2021)
Follow-up paper
Laurent U Perrinet (2019). An adaptive homeostatic algorithm for the unsupervised learning of visual features. Vision.
PDF Cite DOI Code URL

MotionClouds

MotionClouds are random dynamic stimuli optimized to study motion perception.

Web-site
Source code using Python.
This algorithm was presented in the following paper
Paula Sanz Leon, Ivo Vanzetta, Guillaume S Masson, Laurent U Perrinet (2012). Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception. Journal of Neurophysiology.
PDF Cite DOI HAL URL arXiv Supp
3746 citations on Google Scholar (last updated 04/09/2025)
examples of use: https://laurentperrinet.github.io/sciblog/categories/motionclouds.html
Follow-up paper
Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation.
PDF Cite DOI URL arXiv

Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation.
PDF Cite DOI URL arXiv
This library was notably used in the following papers:
Claudio Simoncini, Laurent U Perrinet, Anna Montagnini, Pascal Mamassian, Guillaume S Masson (2012). More is not always better: dissociation between perception and action explained by adaptive gain control. Nature Neuroscience.
PDF Cite DOI URL

Cesar U Ravello, Laurent U Perrinet, Maria-José Escobar, Adrián G Palacios (2019). Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli. Scientific Reports.
Cite DOI Press URL HAL

Hugo Ladret, Nelson Cortes, Lamyae Ikan, Frédéric Chavane, Christian Casanova, Laurent U Perrinet (2023). Cortical recurrence supports resilience to sensory variance in the primary visual cortex. Nature Communications Biology.
PDF Cite DOI Dataset Code URL bioRxiv HAL

PyNN

PyNN is a simulator-independent language for building neuronal network models using Python.

Web-site
Source code
This algorithm was presented in the following paper
Andrew P Davison, Daniel Bruderle, Jochen Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent U Perrinet, Pierre Yger (2008). PyNN: A Common Interface for Neuronal Network Simulators. Frontiers in Neuroinformatics.
PDF Cite Project DOI URL HAL
619 citations on Google Scholar (last updated 22/10/2021)

Tout public!