Neuroscience Cloud Analysis As a Service (NeuroCAAS) is a drag-and-drop data analysis platform for neuroscience. To get started, select one of our available analyses below, and analyze some demo data or bring your own. NeuroCAAS is committed to an open source model; contact us at firstname.lastname@example.org if you are interested in helping to develop or fund NeuroCAAS.
NeuroCAAS is an open-source scientific resource that uses cloud resources to run powerful modern data analyses. NeuroCAAS packages these analyses into fully portable descriptions called blueprints, which can then be deployed to analyze data on demand and automatically. Analyzing data with NeuroCAAS does not require any hardware purchases, installation, or dependency management. As a scientific user, you are responsible for only two things: 1) your data, and 2) a configuration file containing analysis parameters. You can find working demo datasets and configuration files in each of our available analyses. To learn more, check out our github repo and paper below.
Emergent property inference (EPI) is a method for learning parameter distributions on models constrained to produce certain desirable phenomena in their output.
Markerless pose estimation of user-defined features with deep learning for all animals, including humans.
CaImAn is a python toolbox for large scale Calcium Imaging data Analysis and behavioral analysis.
Region-based Decomposition for Widefield Calcium Imaging Data.
Penalized Matrix Decomposition for Denoising and Compression of Functional Imaging Data.
Yet Another Spike Sorter
The Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL) is a method for inferring latent dynamics from single-trial 2-photon (2p) calcium imaging measurements.
Placeholder analysis to allow access to widefield analysis for federated users.