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 currently lacks the ability to perform parameter optimization on rhythmic/bursty and poisson drive configurations. The goal is to add this functionality and generalize the optimization routine to encompass all types of exogenous drive parameters that are used to configure model simulation outputs.
- Develop functions, optimize_rhythmic and optimize_poisson, that can be used to optimize the parameters associated with each of these drive types, respectively. Inspiration can be drawn from the current implementation of optimized_evoked, however, the optimization routines for rhythmic and poisson drives will not necessarily leverage the sequential design used for a sequence of evoked drives.
- 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 efficacy of parameter recovery using current optimization routine · Issue #466 · 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: Ryan Thorpe
Project website: https://hnn.brown.edu/
Backup mentors: Nicholas Tolley, Mainak Jas
Tech keywords: Python, networks, modeling, simulation