We share code and data from our papers. Lab repositories are on GitHub in the VU-Cog-Sci organization.
Python package for fitting population receptive field models (including compressive spatial summation, difference-of-Gaussians and divisive normalization variants) and related models such as connective field and contrast sensitivity models, on GPU-accelerated backends (TensorFlow, PyTorch or JAX, through Keras 3). Developed by the Popylar team with the Netherlands eScience Center.
pip install "prfmodel[tensorflow]"Python package to simulate and fit population receptive field (pRF) models to time series data, including the divisive normalization pRF model. prfmodel, developed in the Popylar project, builds on it.
Python package to estimate the shape of responses in fMRI, pupil and other time series by deconvolution, including hierarchical Bayesian estimation.
Python toolbox for spatiotemporal forward modeling of brain signals. It combines pRF maps from fMRI with MEG to estimate when each visual field map contributes to the measured response.
Tools for planning, reconstructing and preprocessing line-scanning fMRI data at the Spinoza Centre, including the selection of a target location on the cortical surface.
Python package that wraps PsychoPy to build psychophysics and fMRI experiments with accurate timing and logging, with optional eye tracking.
Python package for loading data from the Natural Scenes Dataset (NSD), a 7T fMRI dataset: regions of interest, mapping-experiment results and single-trial response estimates, in volume or surface formats.
Analysis code for the vicarious body maps study, which fits connective field models of somatosensory and visual topography to 7T Human Connectome Project data.
An intensive 7T fMRI dataset of brain responses to 2,160 brief naturalistic videos, with about 21 hours of scanning per participant at 1.7 mm isotropic resolution, plus retinotopy and motion-energy localizers for every participant.
An atlas of the somatotopic body maps described in the vicarious body maps study. The paper's data availability statement describes how to obtain it.
A pycortex subject (999999) for the 59k-vertex surfaces of the Human Connectome Project CIFTI format, for plotting surface data on flatmaps.
Atlas of the retinotopic maps in the human cerebellum (oculomotor vermis, lobule VIIb and lobule VIIIb), derived from Human Connectome Project retinotopy data, in 2 mm MNI space, with the code that produced it.
How-tos for our data, analysis and lab procedures are on the lab wiki. The pages below cover how we work together and write code.
The HCP 59k surfaces (subject 999999) as a pycortex subject, for plotting surface data on flatmaps.
General rules we try to follow in our work.
Basic rules for coding, best practices