Bouchard Lab GitHub's repositories
DynamicalComponentsAnalysis
Dynamical Components Analysis
process_nwb
Functions for preprocessing timeseries data stored in the NWB format
info_measures
Python implementations of information theoretic measures
ReachMaster
A pneumatically-actuated robotic system for complex rodent reaching tasks
neuroinference
Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery
neuropacks
A set of classes to parse various neuroscience datasets.
Contrastive_Predictive_coding
This is a modified version of contrastive predictive coding on time series
sparse_coding
Sparse coding models in pytorch
nsds_lab_to_nwb
Python package to convert NSDS Lab data to NWB files.
template_module_repo
Template repository for a repo for a module/library (not a paper/project)
template_paper_repo
Template repository for a repo for a paper/project (not a module/library)
Compressed-Predictive-Information-Coding
The repo for compressed predictive information coding
concavity
Concave Hull boundary polygon for an array of points and concave and convex polygon vertex detection
ergm
Fit, Simulate and Diagnose Exponential-Family Models for Networks
Gaussian-Process-Regression-Network
Pytorch version of Gaussian Process Regression Network
HangulFontsDatasetGenerator
Scripts to generate the Hangul Fonts Dataset
Natural-Sounds-Visualizations
Python code by Vitto Resnick for importing and visualizing audio files as ndarrays.
nersc_python
Docker images and example slurm scripts
neurobiases
Identifying and mitigating statistical biases in neural models of tuning and functional coupling
Orthogonal-Stochastic-Linear-Mixing-Model
This is the python implementation of the paper [Bayesian Inference in High-Dimensional Time-Series with the Orthogonal Stochastic Linear Mixing Model]. We propose a new regression framework to model multivariate output response data, which not only capture the complex input-dependent correlation across outputs, but also is effient for massive model and capable for single-trial analysis in neural data. Please refer our model for more details.
variational_bounds_of_mutual_information
python version of variational bounds of mutual information