Human Neocortical Neurosolver

We are thrilled and honored to announce that Dr Stephanie Jones has won the Biomag2020 Mid Career Award for her work on biologically-principled mathematical and computational models of MEG/EEG. Please Join us at the Biomag Virtual event 2020 for the award announcement.

Watch Dr. Stephanie Jones' recent presentation
at the 2020 Allen Institute Modeling Workshop

See also a recent presentation by Dr. Jones
on the use of HNN for neuromodulation (presented
at NYC Neuromodulation 2020) and Dr. Jones' keynote
presentation at BrainStim 2020.

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

Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT, Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data (2020). eLife 2020;9:e51214 DOI: 10.7554/eLife.51214 (

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.

Planned Software Expansions

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.

  • Biophysically-principled estimation of local field potential signals in different cortical layers
  • 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
  • Dynamic interaction of primary current sources in multiple cortical areas and thalamic nuclei.

  • Get in Touch

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