1-day current streak·13-day longest streak
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frontiers_2014
Holds the code use in the "Which fMRI clustering gives good brain parcellations?" frontiers 2014 paper.
Python ★ 11 11y agoExplain → -
scikit-learn ⑂
scikit-learn main repo
Python ★ 2 5y agoExplain → -
nipy ⑂
Neuroimaging in python
C ★ 2 10y agoExplain → -
representational_similarity_analysis
Some experiments on the probabilistic control on the inference in Representational Similarity Analyis (RSA)
TeX ★ 1 10y agoExplain → -
nisl.github.com ▣
Web page for NISL: NeuroImaging with scikit-learn
JavaScript ★ 1 15y agoExplain → -
nilearn ⑂
NeuroImaging with the Scikit-learn: fMRI inverse inference tutorial
Python ★ 1 1y agoExplain → -
retinotopic_mapping
Code for analysing fMRI retinotopy data.
Python ★ 1 12y agoExplain → -
regions ⑂
No description.
Python ★ 1 14y agoExplain → -
codeSync ⑂
No description.
Matlab ★ 1 14y agoExplain → -
PySurfer ⑂
Python tools for Freesurfer
Python ★ 1 14y agoExplain → -
yeast-cycle ⑂
Some code for my research.
Python ★ 1 16y agoExplain → -
nipy-artwork ⑂
Fliers, images, etc
★ 1 14y agoExplain → -
nibabel ⑂
Python package to access a cacophony of neuro-imaging file formats
Python ★ 1 15y agoExplain → -
fmralign ⑂
Functional alignment and template estimation library for functional Magnetic Resonance Imaging data
★ 0 3y agoExplain → -
hidimstat ⑂
HiDimStat: High-dimensional statistical inference tool for Python
★ 0 7mo agoExplain → -
hidimstat_old ⑂
HiDimStat: High-dimensional statistical inference tool for Python
Python ★ 0 1y agoExplain → -
fugw_barycenters
Experimental work on FUGX barycenters
Jupyter Notebook ★ 0 2y agoExplain → -
med_bench ⑂
No description.
★ 0 2y agoExplain → -
resting_state_fMRI_quality_assessement
Repo to assess the quality of fMRI data acquired at Neurospin
Python ★ 0 2y agoExplain → -
pytorch-scarf ⑂
Implementation of SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption in Pytorch, a model learning a representation of tabular data using contrastive learning. It is inspired from SimCLR and uses a similar architecture and loss.
Python ★ 0 2y agoExplain → -
deep_parcellation_benchmark
Work related to the publication on A deep-phenotyping-based benchmark for parcellations
Python ★ 0 3y agoExplain → -
misc_material
No description.
★ 0 3y agoExplain → -
main-educational.github.io ⑂
website of the educational workshop of the Montreal Artificial Intelligence and Neuroscience (MAIN) conference
★ 0 3y agoExplain → -
brain_decoding ⑂
A jupyter book on brain decoding using functional magnetic resonance imaging
★ 0 3y agoExplain → -
connectivity_predict
No description.
Python ★ 0 3y agoExplain → -
mtw ⑂
Wasserstein regularization for sparse multi-task regression
Python ★ 0 3y agoExplain → -
admin ⑂
Admin discussion relative to Nilearn
TeX ★ 0 2y agoExplain → -
sanssouci.python ⑂
Post hoc inference via multiple testing
★ 0 7mo agoExplain → -
pyrft ⑂
No description.
Python ★ 0 5y agoExplain → -
functional_prediction
No description.
Python ★ 0 5y agoExplain → -
OHBM2020_ML4NI ⑂
OHBM2020 educational course
★ 0 6y agoExplain → -
dev-days-2020 ⑂
Web site for the Nilearn Dev Days, May 2020
★ 0 6y agoExplain → -
nistats ⑂
Modeling and statistical inference on fMRI data in Python
Python ★ 0 6y agoExplain → -
ibc_55b
Some works on the investiagtion of area 55b based on Individual Brain Charting (IBC) dataset
Python ★ 0 7y agoExplain → -
trialwise_glms
No description.
Python ★ 0 8y agoExplain → -
Datasense-2018
No description.
TeX ★ 0 8y agoExplain → -
nilearn_sandbox ⑂
Playground for nilearn compatible features.
Python ★ 0 10y agoExplain → -
pypreprocess ⑂
Preprocessing scripts for neuro imaging
Python ★ 0 2y agoExplain → -
ipmi_2015
Experiments on the "Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations" IPMI 2015 paper
Python ★ 0 11y agoExplain → -
mathematicians
Analysis of the mathematicians fMRI dataset
Python ★ 0 11y agoExplain → -
fMRI_PCR
Prediction of functional responses across individuals from Principal Components Regression
Python ★ 0 12y agoExplain →
No repos match these filters.