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): https://groups.google.com/g/hnnsolver
Goal: The aim of this project is to enhance HNN functionality with tools to simulate and visualize current source density (CSD) signals from the HNN neocortical model, beginning with the HNN-core API for simulating local field potential signals (LFPs).
Subgoals
Related issue: https://github.com/jonescompneurolab/hnn-core/issues/68
- Understand the LFP example and code
- Decide on CSD methods and API. For example: standard difference of LFP, spline, step etc. See the iCSD package
- Develop the code for documenting CSD tools following the current examples for simulation event relate potentials and low frequency rhythms
- Write tests for the CSD functionality. For example, by computing the CSD on artificial “sinusoidal” LFP pattern, or checking that the peaks are roughly the same with different methods.
- Bonus: Build local fields potential and CSD visualization into the next generation HNN GUI components in https://github.com/jonescompneurolab/hnn-core/pull/76. Begin implementation of comparison of simulated LFP/CSD to empirically recorded data.
Difficulty: Medium
Duration: 350 hours (full time)
Skills: Some experience in neuroscience and Python. Experience in simulating neural activity with NEURON is helpful but not required.
Possible mentors: Nicholas Tolley, Stephanie Jones @Stephanie_Jones, Mainak Jas, Ryan Thorpe
Tech keywords: Python
