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), (Pasturel et al, 2020), (Daucé 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.
bayesianchangepoint
An implementation of Adams & MacKay 2007 “Bayesian Online Changepoint Detection” for binary inputs in Python.
- Source code
- See the final publication @
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)
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.
PyNN
PyNN is a simulator-independent language for building neuronal network models.
Laurent U Perrinet
Researcher in Computational Neuroscience
My research interests include Machine Learning and computational neuroscience applied to Vision.
