Challenge in Human Neuroscience

Electro- and magneto-encephalography (EEG/MEG) are among the most powerful technologies to non-invasively record human brain activity with millisecond resolution. They provide reliable markers of healthy brain function and disease states. A major limitation is that it is often difficult to connect the macroscopic scale measured signals to the underlying cellular and circuit level neural generators. This difficulty limits the translation of EEG/MEG studies into novel principles of information processing, or into new treatment modalities for neural pathologies.

 Solution: Human Neocortical Neurosolver

Human Neocortical Neurosolver (HNN) is a user-friendly software tool that provides a novel solution to this challenge. HNN gives researchers and clinicians the ability to test and develop hypotheses on the circuit mechanism underlying their EEG/MEG data in an easy-to-use environment. The foundation of HNN is a computational neural model that simulates the electrical activity of the neocortical cells and circuits that generate the primary electrical currents underlying EEG/MEG recordings. We provide tutorials on how to import your data and to begin to understand the underlying circuit mechanisms.

 Getting Started

Learn about the model on the Overview and Uniqueness page

Review our step-by-step instructions on the Tutorials page

Our tutorials are based on prior studies of primary somatosensory cortex and focus on simulating some of the most commonly measured EEG/MEG signals including sensory evoked response (ERPs), and low frequency alpha (10-14 Hz), beta (15-30 Hz) and gamma (30-80 Hz) rhythms (Jones et al., 2007; Jones et al., 2009; Sherman et al., 2016; Lee & Jones 2013).  By learning how to simulate these basic signals, you will gain insight into how to adjust the parameters in our model for your hypothesis-testing needs.

 Citing Our Software

Samuel A Neymotin, Dylan S Daniels, Blake Caldwell, Noam Peled, Robert A McDougal, Nicholas T Carnevale, Christopher I Moore, Michael L Hines, Matti Hamalainen, Stephanie R Jones. Human Neocortical Neurosolver (2018), DOI 10.5281/zenodo.1446517. 

 We’re Open Source!

Our software was created to be expandable and generative. While designed for use by researchers without computational neuroscience expertise, if you know a bit about computational neuroscience, we encourage you to contribute to the developments. Our software is open-source. It was created in the NEURON-Python simulation environment and designed to be flexibly expanded to include other cell types, or circuits. We have a user’s forum and online resources to answer questions and expand the utility of our tool.

Software Expansions Coming Soon

 We are planning several expansions to our code. We value the input of the user to guide our developments. Please join our user’s forum to submit your suggestions to extend HNN’s capabilities.

  • Integration with MNE-Python inverse solution package will enable estimation of the location, timecourse, and circuit mechanisms underlying signal generation all in one easy-to-use software
  • MNE-Python will soon enable source localization of ECoG signals, expanding the utility of HNN to subdural recordings
  • Biophysically-principled estimation of local field potential signals in different cortical layers
  • Dynamic interaction of primary current sources in multiple cortical areas and thalamic nuclei.
  • Integration with the Neuroscience Gateway Portal will allow users to run simulations directly on the web to avoid downloading and installing the software and to reduce simulation run-times.
  • Parameter optimization will allow users to specify a set of parameters that can be automatically optimized to fit recorded data.

Join our Google Group

Get In Touch

Stephanie Jones Laboratory
Carney Institute for Brain Sciences
Brown University
164 Angell St., Floor 4
Providence, RI 02906