Leadership Alliance Summer Student Ideas Page
Human Neocortical Neurosolver (HNN) is a software for interpreting the neural origin of macroscale MEG/EEG data using biophysically-detailed microcircuit simulations. HNN can be run through a user-friendly graphical user interface or through a command line interface using Python.
IRC channel:
Mailing list(s): https://groups.google.com/g/hnnsolver
- Overview of HNN Utility
- HNN-GUI tutorials
- HNN-core command line tutorials and examples
- Contributing guide
Medium
Some experience in neuroscience and Python. Experience in simulating neural activity with NEURON is helpful but not required.
Mainak Jas, Stephanie Jones, Nicholas Tolley, Ryan Thorpe
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).
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.
