Open Science

Snapshot of a Motion Cloud

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) or courses and slides (e.g. 2019-04-03_a_course_on_vision_and_modelization, 2019-04-18_JNLF, …) and also the development of the following libraries on GitHub.

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bayesianchangepoint

An implementation of Adams & MacKay 2007 “Bayesian Online Changepoint Detection” in Python.

LeCheapEyeTracker

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

Biologically inspired computer vision

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

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.

This algorithm was presented in the following paper, which is available as a reprint @ https://laurentperrinet.github.io/publication/perrinet-15-bicv/

SparseHebbianLearning : 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/).

MotionClouds

MotionClouds are random dynamic stimuli optimized to study motion perception.

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Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.

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