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https://jonescompneurolab.github.io/hnn/

The Human Neocortical Neurosolver (HNN) is a software tool that gives researchers/clinicians the ability to develop/test hypotheses on circuit mechanisms underlying EEG/MEG data.
https://jonescompneurolab.github.io/hnn/

alpha beta biophysical brain computational-neuroscience data-analysis eeg electrophysiology erp gamma human interneuron meg neocortex neuronal-network pyqt5 pyramidal-neuron python simulation-modeling

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The Human Neocortical Neurosolver (HNN) is a software tool that gives researchers/clinicians the ability to develop/test hypotheses on circuit mechanisms underlying EEG/MEG data.

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# Human Neocortical Neurosolver (HNN)



## About



The **Human Neocortical Neurosolver (HNN)** is an open-source neural modeling tool designed to help

researchers/clinicians interpret human brain imaging data. HNN presents a convenient GUI to an

anatomically and biophysically detailed model of human thalamocortical brain circuits, which

makes it easier to generate and evaluate hypotheses on the mechanistic origin of signals measured

with MEG/EEG or intracranial ECoG. A unique feature of HNN's model is that it accounts for the

biophysics generating the primary electric currents underlying such data, so simulation results

are directly comparable to source localized data (nano-Ampere-meters); this enables precise

tuning of model parameters to match characteristics of recorded signals.



We are integrating the circuit-level modeling with the [minimum-norm-estimate (MNE) source

localization software](https://martinos.org/mne/stable/index.html), so researchers can compute

MEG/EEG source estimates and test hypotheses on

the circuit origin of their data in one software package. Our goal is to design HNN to be useful

to researchers with no formal computational neural modeling or coding experience.



For more information visit [https://hnn.brown.edu](https://hnn.brown.edu) . There, we describe the use of HNN in studying the

circuit-level origin of some of the most commonly measured MEG/EEG and ECoG signal: event related

potentials (ERPs) and low frequency rhythms (alpha/beta/gamma).



## Installation



Please follow the links on our [installation page](installer) to find instructions for your operating system.



## Quickstart



Just do:



    $ python hnn.py



to start the HNN graphical user interface



## Command-line usage



HNN is not designed to be invoked from the command line, but we have started

[hnn-core](https://jonescompneurolab.github.io/hnn-core), a new Python project that can run

simulations with native Python code. Dipole and spiking data are stored in Python objects

and some plotting functions have been implemented. Future versions of this code (HNN) will

import the `hnn-core` module for running simulations.



## Questions



For questions, comments/feedback, or troubleshooting information please contact

us at [email protected], and review our user forum at

[https://www.neuron.yale.edu/phpBB/viewforum.php?f=46](https://www.neuron.yale.edu/phpBB/viewforum.php?f=46) .



## References



To cite the HNN software please use the following references:



 [eLife 2020;9:e51214 DOI: 10.7554/eLife.51214](https://doi.org/10.7554/eLife.51214)

 and

 [![DOI](https://zenodo.org/badge/128077928.svg)](https://zenodo.org/badge/latestdoi/128077928)



[![Build Status](https://travis-ci.com/jonescompneurolab/hnn.svg?branch=master)](https://travis-ci.com/jonescompneurolab/hnn)