The Allen Institute on Github

The Allen Institute Github space hosts software packages that use or support access to online projects, resources, publications and data sets affiliated with the Allen Institute.

Allen SDK

The Allen Institute for Brain Science has published the Allen Software Development Kit (SDK), which houses source code for reading and processing Allen Brain Atlas data. The Allen SDK focuses primarily on tools and analysis associated with the Allen Cell Types Database, Mouse Connectivity Atlas, and Allen Brain Observatory.

Allen Brain Atlas API

The Allen Institute for Brain Science offers access to its published data through an application programming interface (API). The API, documentation, and sample applications are made available to the community under the Allen Institute's Terms of Use. The Allen SDK provides Python classes, notebooks, and code samples to facilitate API use.

Research & Publications

Scientists and engineers at the Allen Institute for Brain Science sometimes develop software tools and analysis platforms for projects that are not available via http://www.brain-map.org. Code is made available through GitHub for prototyping, user feedback or as supplementary files associated with research publication. The projects available on GitHub include the NWB API, an early prototype Python library developed as part of the Neurodata Without Borders: Neurophysiology (NWB) project. Code developed for Generalized Leaky Integrate and Fire (GLIF) and biophysical models derived from data in the Allen Cell Types Database is also available.

Brain Modeling Toolkit (BMTK)

The Brain Modeling Toolkit (BMTK) is a python-based software package for creating and simulating large-scale neural network models. It supports building, simulation and analysis of models at different levels of resolution including biophysically detailed networks, point neuron networks, filter models, and population-level networks.

DiPDE

DiPDE (dipde) is a simulation platform for numerically solving the time evolution of coupled networks of neuronal populations. Instead of solving the subthreshold dynamics of individual model leaky-integrate-and-fire (LIF) neurons, dipde models the voltage distribution of a population of neurons with a single population density equation. In this way, dipde can facilitate the fast exploration of mesoscale (population-level) network topologies, where large populations of neurons are treated as homogeneous with random fine-scale connectivity.