awesome-neural-data-tools

Tools, algorithms, and frameworks for managing and analyzing neural data (derived from original notes from the 2018 BRAIN Initiative Investigators Meeting by Liam Paninski)

Calcium+voltage imaging:

• https://github.com/flatironinstitute/CaImAn
• https://github.com/zhoupc/CNMF_E
• https://github.com/cortex-lab/Suite2P
• https://github.com/jewellsean/FastLZeroSpikeInference
• https://github.com/losonczylab/sima
• https://www.biorxiv.org/content/early/2018/05/30/334706 (compression + denoising)
• https://github.com/bahanonu/calciumImagingAnalysis - Biafra Ahanonu (MIT)

Spike sorting:

• https://github.com/cortex-lab/KiloSort
• https://github.com/mouseland/kilosort2
• http://www.jrclust.org/
• https://github.com/flatironinstitute/ironclust
• https://github.com/paninski-lab/yass
• https://github.com/flatironinstitute/mountainsort
• https://github.com/pillowlab/BinaryPursuitSpikeSorting
• https://spyking-circus.readthedocs.io/en/latest/
• http://homepages.inf.ed.ac.uk/mhennig/herdingspikes/
• https://github.com/tridesclous/tridesclous
• http://neuralensemble.org/neo/ - Intracellular recording

Optogenetic stimulation

• https://github.com/wjyangGithub/Holographic-Photostimulation-System (matlab tools for SLM targeting and hologram generation)

Behavioral + stimulus control (and analysis)

• https://bonsai-rx.org/
• http://www.openmaze.org/
• https://pni.princeton.edu/pni-software-tools/virmen
• https://github.com/dendritic/signals
• autopilot (docs, repo, paper) - a distributed Python framework for complex behavioral neuroscience experiments built on the Raspberry Pi <3

Fluorescence Imaging

• http://ilastik.org/index.html

Compression

• http://blosc.org/
• https://bellard.org/bpg/
• https://github.com/int-brain-lab/mtscomp

Registration & stitching

• https://github.com/neurodata/ndreg - current reference registration for cleared brain data
• http://stnava.github.io/ANTs/ - reference for much human MRI
• https://github.com/khaledkhairy/EM_aligner
• https://abria.github.io/TeraStitcher/ - current reference implementation for linear stitching
• https://github.com/SainsburyWellcomeCentre/amap-python - automated mouse whole-brain registration

Electron microscopy

• https://github.com/google/neuroglancer WebGL-based viewer for volumetric data
• https://github.com/seung-lab/cloud-volume (for working with "precomputed" format of Neuroglancer)
• https://github.com/seung-lab/Alembic (aligning images of serial sections)
• https://github.com/saalfeldlab/render - current reference implementation for dynamic rendering of aligned data

scRNAseq / FISH

• https://github.com/spacetx/starfish

Eye tracking

• https://github.com/AllenInstitute/allensdk.eye_tracking
• https://github.com/kristinbranson/APT
• https://github.com/mkrumin/EyeTracking
• https://github.com/carsen-stringer/FaceMap

Animal part tracking

• https://github.com/kristinbranson/APT
• http://ilastik.org/
• https://github.com/AlexEMG/DeepLabCut
• https://github.com/carsen-stringer/FaceMap
• https://www.biorxiv.org/content/early/2018/05/30/331181

Statistical Machine Learning

Spike train data analysis methods - encoding models, decoders, dimensionality reduction, etc

• http://xarray.pydata.org/en/stable/
• RF estimation (ASD prior): https://github.com/pillowlab/fastASD
• GLM fitting for trial-based data: https://github.com/pillowlab/neuroGLM
• Entropy Estimation: https://github.com/pillowlab/CDMentropy/
• Hypothesis testing: https://github.com/neurodata/mgc
• Linear dimensionality reduction: https://github.com/neurodata/LOL
• Classification and regression: https://github.com/neurodata/R-RerF

Visualization (2D, 3D, 4D, 5D)

Web-Viz

• http://catmaid.readthedocs.io/en/stable/
• https://github.com/google/neuroglancer
• http://www.geppetto.org/

Local-Viz

• https://imagej.net/BigDataViewer
• http://www.alleninstitute.org/what-we-do/brain-science/research/products-tools/vaa3d/
• https://github.com/BrancoLab/BrainRender - brainrender 3d neuroanatomical renderings in python

Pipelines, data sharing, reproducibility

Tracking provenance of data / analysis outputs. Automatic repopulation of databases as analysis algorithms are updated. Store both data and model simulation output

• https://datajoint.github.io/

Data specifications

• https://neurodatawithoutborders.github.io/
• http://bids.neuroimaging.io/

Reproducibility + knowledge sharing tools

• https://mybinder.org/
• http://gigantum.io/
• https://colab.research.google.com/notebooks/welcome.ipynb
• https://www.kaggle.com/kernels

Data/Tool Repositories:

• https://crcns.org/
• https://neurodata.io/
• https://www.nitrc.org/
• http://datalad.org/
• https://portal.brain-map.org/

Review articles

• Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience; Liam Paninski, John Cunningham https://www.biorxiv.org/content/early/2017/10/02/196949