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arhmm-celegans

b-soid

Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.

bams

PyTorch implementation of BAMS (https://multiscale-behavior.github.io/)

cs228-notes

Course notes for CS228: Probabilistic Graphical Models.

deep-learning-experiments

Notes and experiments to understand deep learning concepts

dynamax

State Space Models library in JAX

evidenceapprox

Evidence approximation methods in SSMs

fly-vr

FlyVR is code for design and control of multisensory virtual reality experiments for flies.

glm_utils

glmhmm

code to fit GLMs, HMMs, and GLM-HMMs (aka IO-HMMs)

jax-moseq

jpca

jPCA for Neural Data Analysis in Python

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

keypoint-moseq

keypointmoseq

motionmapperpy

Modified Python 3.0 implementation of MotionMapper (https://github.com/gordonberman/MotionMapper)

natneuro-latex-template

LaTeX manuscript template based on a Nature Neuroscience submission

pml2-book

Probabilistic Machine Learning: Advanced Topics

pyglmhmm

A Python implementation of GLM-HMM (port of https://github.com/murthylab/GLMHMM)

pytorch-deep-learning

Deep Learning (with PyTorch)

pytorch-the-hard-way

r-shruthi11.github.io

sleap-io

Standalone utilities for SLEAP pose tracking data.

som-vae

TensorFlow implementation of the SOM-VAE model as described in https://arxiv.org/abs/1806.02199

ssm

Bayesian learning and inference for state space models

stats271sp2021

Material for STATS271: Applied Bayesian Statistics (Spring 2021)

test_screen_calibration

vame

Variational Animal Motion Embedding