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

Simulation and optimization of neural circuits for MEG/EEG source estimates
https://jonescompneurolab.github.io/hnn-core/

computational-modeling eeg meg neuron-simulator

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Simulation and optimization of neural circuits for MEG/EEG source estimates

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README 

        
hnn-core

========



|tests| |CircleCI| |Codecov| |PyPI| |Gitter| |JOSS|



|HNN-GUI|



About

-----

This is a leaner and cleaner version of the code based off the

`HNN repository `_.



The **Human Neocortical Neurosolver (HNN)** is an open-source neural modeling

tool designed to help researchers/clinicians interpret human brain imaging

data. Based off the original

`HNN repository `_, **HNN-core**

provides a convenient way to run simulations of an anatomically

and biophysically detailed dynamical system model of human thalamocortical

brain circuits with only a few lines of code. Given its modular,

object-oriented design, HNN-core makes it easy to generate and evaluate

hypotheses on the mechanistic origin of signals measured with

magnetoencephalography (MEG), electroencephalography (EEG), or

intracranial electrocorticography (ECoG). A unique feature of the HNN 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 (current dipoles in units of nano-Ampere-meters); this enables

precise tuning of model parameters to match characteristics of recorded

signals. Multimodal neurophysiology data such as local field potential (LFP),

current-source density (CSD), and spiking dynamics can also be simulated

simultaneously with current dipoles.



While the HNN-core API is designed to be flexible and serve users with varying

levels of coding expertise, the HNN-core GUI is designed to be useful

to researchers with no formal computational neural modeling or coding

experience.



For more information visit `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 signals: event related

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



Contributors are very welcome. Please read our

`contributing guide`_ if you are interested.



Dependencies

------------

hnn-core requires Python (>=3.8) and the following packages:



* numpy

* scipy

* matplotlib

* Neuron (>=7.7)



Optional dependencies

---------------------



GUI

~~~



* ipywidgets

* voila

* ipympl

* ipykernel



*Note*: Please follow the **GUI installation** section to install the correct

GUI dependency versions automatically.



Optimization

~~~~~~~~~~~~



* scikit-learn



Parallel processing

~~~~~~~~~~~~~~~~~~~



* joblib (for simulating trials simultaneously)

* mpi4py (for simulating the cells in parallel for a single trial). Also depends on:



  * openmpi or other mpi platform installed on system

  * psutil



Installation

============



We recommend the `Anaconda Python distribution `_.

To install ``hnn-core``, simply do::



   $ pip install hnn_core



and it will install ``hnn-core`` along with the dependencies which are not already installed.



Note that if you installed Neuron using the traditional installer package, it is recommended

to remove it first and unset ``PYTHONPATH`` and ``PYTHONHOME`` if they were set. This is

because the pip installer works better with virtual environments such as the ones provided by ``conda``.



If you want to track the latest developments of ``hnn-core``, you can install the current version of the code (nightly) with::



   $ pip install --upgrade https://api.github.com/repos/jonescompneurolab/hnn-core/zipball/master



To check if everything worked fine, you can do::



   $ python -c 'import hnn_core'



and it should not give any error messages.



**Installing optimization dependencies**



If you are using bayesian optimization, then scikit-learn is required. Install 

hnn-core with scikit-learn using the following command::



    $ pip install hnn_core[opt]



**GUI installation**



To install the GUI dependencies along with ``hnn-core``, a simple tweak to the above command is needed::



   $ pip install hnn_core[gui]

   

Note if you are zsh in macOS the command is::



   $ pip install hnn_core'[gui]'



To start the GUI, please do::



   $ hnn-gui



**Parallel backends**



For further instructions on installation and usage of parallel backends for using more

than one CPU core, refer to our `parallel backend guide`_.



**Note for Windows users**



Install Neuron using the `precompiled installers`_ **before** installing

``hnn-core``. Make sure that::



   $ python -c 'import neuron;'



does not throw any errors before running the install command.

If you encounter errors, please get help from `NEURON forum`_.

Finally, do::



   $ pip install hnn_core[gui]



Documentation and examples

==========================



Once you have tested that ``hnn_core`` and its dependencies were installed,

we recommend downloading and executing the

`example scripts `_

provided on the `documentation pages `_

(as well as in the `GitHub repository `_).



Note that ``python`` plots are by default non-interactive (blocking): each plot must thus be closed before the code execution continues. We recommend using and 'interactive' python interpreter such as ``ipython``::



   $ ipython --matplotlib



and executing the scripts using the ``%run``-magic::



   %run plot_simulate_evoked.py



When executed in this manner, the scripts will execute entirely, after which all plots will be shown. For an even more interactive experience, in which you execute code and interrogate plots in sequential blocks, we recommend editors such as `VS Code `_ and `Spyder `_.



Bug reports

===========



Use the `github issue tracker `_ to

report bugs. For user questions and scientific discussions, please join the

`HNN Google group `_.



Interested in Contributing?

===========================



Read our `contributing guide`_.



Roadmap

=======



Read our `roadmap`_.



Citing

======



If you use HNN-core in your work, please cite our

`publication in JOSS `_:



   Jas et al., (2023). HNN-core: A Python software for cellular and

   circuit-level interpretation of human MEG/EEG. *Journal of Open Source

   Software*, 8(92), 5848, https://doi.org/10.21105/joss.05848



.. _precompiled installers: https://www.neuron.yale.edu/neuron/download

.. _NEURON forum: https://www.neuron.yale.edu/phpbb/

.. _contributing guide: https://jonescompneurolab.github.io/hnn-core/dev/contributing.html

.. _parallel backend guide: https://jonescompneurolab.github.io/hnn-core/dev/parallel.html



.. |tests| image:: https://github.com/jonescompneurolab/hnn-core/actions/workflows/unit_tests.yml/badge.svg?branch=master

   :target: https://github.com/jonescompneurolab/hnn-core/actions/?query=branch:master+event:push



.. |CircleCI| image:: https://circleci.com/gh/jonescompneurolab/hnn-core.svg?style=svg

   :target: https://circleci.com/gh/jonescompneurolab/hnn-core



.. |Codecov| image:: https://codecov.io/gh/jonescompneurolab/hnn-core/branch/master/graph/badge.svg

   :target: https://codecov.io/gh/jonescompneurolab/hnn-core



.. |PyPI| image:: https://img.shields.io/pypi/dm/hnn-core.svg?label=PyPI%20downloads

   :target: https://pypi.org/project/hnn-core/



.. |HNN-GUI| image:: https://user-images.githubusercontent.com/11160442/226248652-1711cdf4-f72b-439e-b4bb-15677fbe6ea5.png



.. |Gitter| image:: https://badges.gitter.im/jonescompneurolab/hnn_core.svg

   :target: https://gitter.im/jonescompneurolab/hnn-core?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge



.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.05848/status.svg

   :target: https://doi.org/10.21105/joss.05848