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3dbrainvisualization

datajoint-elements

DataJoint Elements is a collection of curated computational workflows for neurophysiology experiments.

datajoint-pipeline-and-strf-visualization-

datajoint-utilities

A general purpose repository containing all generic tools/utilities surrounding the DataJoint ecosystem. These can be candidates for DataJoint plugins

depthmapping

Using a Kinect camera to create a depth mapping algorithm

djarchive-client

DataJoint Archive Client

djbase-docker

A minimal base docker image with DataJoint Python dependencies installed.

djlab-docker

A docker image optimized for running a JupyterLab environment with DataJoint Python.

djsciops-cicd

ecephys_spike_sorting

Modules for processing extracellular electrophysiology data from Neuropixels probes

element-animal

DataJoint Element for Animal Management - NIH U24

element-array-ephys

DataJoint Element for Extracellular Array Electrophysiology - NIH U24

element-calcium-imaging

DataJoint Element for multi-photon calcium imaging analysis with CaImAn and Suite2p

element-deeplabcut

DataJoint Element for behavioral analysis with DeepLabCut

element-electrode-localization

DataJoint Element for localizing Neuropixels electrodes

element-event

DataJoint Element for event-based behavior experiments

element-facemap

DataJoint Element for behavioral analysis with Facemap

element-interface

Common functions for the DataJoint Elements

element-lab

DataJoint Element for Lab Management - NIH U24

element-miniscope

DataJoint Element for miniscope calcium imaging analysis with CaImAn

element-optogenetics

DataJoint Element for optogenetic experiments

element-session

DataJoint Element for Session Management - NIH U24

facemap

Framework for predicting neural activity from mouse orofacial movements tracked using a pose estimation model. Package also includes singular value decomposition (SVD) of behavioral videos.

git_tutorial

microns_phase3_nda

nda schema for MICrONS phase3

miniconda3-docker

A minimal base docker image with conda.

neural-data-augmentation-model

Project for Beng 189 class at UCSD. Data augmentation on neural data for Neural network

neural-decoding-of-m1-neuron

Created a novel method to augment neural spike data to build a large training set from a limited sample size. Implemented Kalman filter model to predict arm position based on neural activity, and used our augmented dataset to improve model accuracy.

np_data_validation

Tools for validating data integrity across the lifecycle of an ecephys session file

probeinterface

Python package to handle probe layout, geometry and wiring to device.