GSoC 2022 Project Idea 12.3: Develop IO routines for HNN-core outputs (350 h)

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.

Mailing list(s):

Goal: 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

Difficulty: Medium

Duration: 350 hours (full time)

Skills: Python, some experience in neuroscience data analysis may be helpful

Possible mentors: Ryan Thorpe, Nicholas Tolley

Tech keywords: Python

This project idea really interests me, can you please guide me further.

Hi @imad08 ,

Please did you get a chance to go through the various resources that the mentors have shared in the project idea such as tutorials/overviews. Please go through them and also study the project idea that is shared. If you have any specific queries/doubts, feel free to ask the mentors (that are tagged in the post above) or reach out through the email list shared in the idea

Thanks and all the best