Code for biophysical simulation of a cortical column using Neuron
This is a leaner and cleaner version of the code based off the HNN repository.
Contributors are very welcome. Please read our contributing guide if you are interested.
hnn-core requires Python (>=3.7) and the following packages:
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
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.
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:
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:
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.
Interested in Contributing?
Read our contributing guide.
Read our roadmap.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.