Mathis Lab @ EPFL

CEBRA

Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA

AmadeusGPT

We turn natural language descriptions of behaviors into machine-executable code

DLC2Kinematics

a module for kinematic analysis of deeplabcut outputs

Camera_Control

a Python API to record video & system timestamps from Imaging Source USB cameras

CellSeg3D

A napari plugin for direct 3D cell segmentation -- taking you through training, inference, and review of masks

cebra-figures

Figures, tables and stats for Schneider, Lee and Mathis 2022: Learnable latent embeddings for joint behavioral and neural analysis.

CEBRA-demos

CEBRA Demo Notebooks. Please see all of them at the URL below:

modelzoo-figures

SuperAnimal models supporting data

JoystickControlSystem

joystick system used in Mathis et al, 2017

DeepLabCut

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

DeepLabCut-Workshop-Materials

Workshop material for using DeepLabCut

.github

Docker4DeepLabCut2.0

Docker container for running DeepLabCut2.0 (linux support only)

SplitWheelVR

follow along for updates and information on our 2-wheel VR system for mice

CEBRA-assets

Web assets for CEBRA (cebra.ai) and CEBRA DOCS!

ml-agents

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

SegementationLabeler