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
● Overview of HNN Utility
● HNN-GUI tutorials
● HNN-core tutorials and examples
● Contributing guide
● HNN-SBI preprint
Goal
HNN-core currently lacks the ability to simulate large batches of simulations which is critical for parameter optimization. The goal is to add this functionality and develop tutorials on optimization techniques that leverage batch simulations, namely simulation based inference (SBI), a deep learning based Bayesian inference method.
Subgoals
- Develop batch simulation functionality to facilitate parameter sweeps over a range of model parameters
- Create tutorials demonstrating how to use simulation based inference (SBI) with HNN-core
- Consolidate the different optimization functions as much as possible, producing a clear API with minimal redundancies. This should also allow the user to constrain parameter ranges and run simple parameter sweeps by specifying or eliminating the optimization cost function.
- Create a function for visualizing the parameter changes pre-to-post optimization.
- Document the optimization routines with examples and develop tests for each function.
Related issues:
- add functionality for running batch simulations · Issue #140 · jonescompneurolab/hnn-core · GitHub
- Model optimization for rhythmic time courses · Issue #176 · jonescompneurolab/hnn-core · GitHub
- create visualization plot for parameter optimization · Issue #423 · jonescompneurolab/hnn-core · GitHub
- Optimization with calcium model · Issue #567 · 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 (nicholas_tolley@brown.edu)
Project website: https://hnn.brown.edu/
Backup mentors: Ryan Thorpe (ryan_thorpe@brown.edu), Mainak Jas (mainakjas@gmail.com)
Tech keywords: Python, networks, modeling, simulation
