Human Neocortical Neurosolver (HNN) is a software for interpreting the neural origin of macroscale magneto-/electro-encephalography (MEG/EEG) data using biophysically-detailed microcircuit simulations. HNN can be run through a user-friendly graphical user interface or through a Python interface HNN-core.
IRC channel: https://gitter.im/jonescompneurolab/hnn-core
Mailing list(s): https://groups.google.com/g/hnnsolver
● HNN-core tutorials and examples
The current IO routines in HNN-core are fragmented as they were adapted from HNN-GUI. The goal is to develop IO routines adapted from HNN-core objects while maintaining backwards compatibility with HNN-GUI.
- Develop a method to write cell_response object and read from it. It should be able to handle multiple trials and be able to plot rasters after reading from a saved file.
- Develop a function to write and read from Dipole and ExtracellularArray. It should be able to handle multiple trials. The format should be standardized between the two objects as much as possible.
- Develop a function to write and read Network object. It should be based on hdf5 and use the h5io library.
- Document each of the IO formats in an rst document and develop tests for each function.
- Bonus: Develop a function to write Network object to NeuroML format and test that it can be loaded in NetPyne
Related issues: IO for network object · Issue #465 · jonescompneurolab/hnn-core · GitHub Internal data containers and saving simulation results to disk · Issue #341 · jonescompneurolab/hnn-core · GitHub
Skill level: Intermediate
Required skills: Python, some experience in neuroscience data analysis may be helpful
Time commitment: Full-time (350 hours)
Lead mentor: Nicholas Tolley
Project website: https://hnn.brown.edu/
Backup mentors: Ryan Thorpe
Tech keywords: Python, networks, modeling, simulation