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.
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.
Work-in-progress : an eye tracker based on webcams.
Biologically inspired computer vision
SLIP: a Simple Library for Image Processing
LogGabor: a Simple Library for Image Processing
SparseEdges: sparse coding of natural images
Our goal here is to build practical algorithms of sparse coding for computer vision.
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 are random dynamic stimuli optimized to study motion perception.
PyNN is a simulator-independent language for building neuronal network models.
- Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
- The characteristics of microsaccadic eye movements varied with the change of strategy in a match-to-sample task
- Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception
- Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception
- Effect of image statistics on fixational eye movements