maurice-2004-code

Copy of (Maurice et al., 2004) code for NEURON and NetPyNE beginners

This is a copy of the NEURON code available on ModelDB at http://modeldb.yale.edu/98005 corresponding to part of the work in the following paper:

Maurice N, Mercer J, Chan CS, Hernandez-Lopez S, Held J, Tkatch T, Surmeier DJ (2004) D2 dopamine receptor-mediated modulation of voltage-dependent Na+ channels reduces autonomous activity in striatal cholinergic interneurons. J Neurosci 24:10289-301

mosinit.hoc is the original file used to perform the simulation in native NEURON, init.py is a translation of the original simulation code for use with the Python interface of NEURON, and either netpyne_init.py or netpyne_batch.py in the folder netpyne-batch-running can be used to run another translation of this code using the powerful NetPyNE interface to NEURON. For the original Readme, see the file readme.txt. I am not one of the original authors of this code, but it was published open source and unlicensed, so let me know if there's a problem.

Instructions

The easiest way to get started that works ANYWHERE is:

1. Install Anaconda from here: https://www.anaconda.com/products/individual and restart your computer afterwards.
2. Open up a Terminal/Command-line window.
3. Create a compatible "conda" environment with Python 3.7 using a command similar to

conda create --name=neuro python=3.7

4. From now on, you will have to enter or "activate" this environment when you want to use it via

conda activate neuro

Do this now. Afterwards, you should now see something like (neuro) to the left of your command prompt every time it appears.

5. If on Linux, install a necessary library by running conda install gxx_linux-64. If on Windows or Mac/OS X, try installing a similar library via conda install clangxx; I'm not sure whether or not the non-Linux solution will work.
6. Now, install the Python package for NEURON using

pip install neuron

7. Use the command cd to Change Directory into where you've downloaded this model.
8. Run the command nrnivmodl. It should spit out a bunch of text and create a bunch of compiled files in a folder called x86_64. This is it compiling the mechanisms, but not the sim code itself.
9. If you want to run the original NEURON/hoc model code, run nrngui mosinit.hoc. If you want to run my native Python implementation, run nrniv init.py.
10. If you want to run my NetPyNE single-simulation implementation, after you do the above, run pip install netpyne, then cd netpyne-batch-running, then nrnivmodl, and then finally python netpyne_single.py.

SCC Cluster / NetPyNE

If you want to use NetPyNE's support of automatic batch job distribution on the SCC cluster, there's a couple extra steps:

1. First, let's get Anaconda working on the cluster, where it's already pre-installed, but must be configured (following https://www.bu.edu/tech/support/research/software-and-programming/common-languages/python/anaconda/ ). Create a folder called conda_envs in your personal /projectnb directory, i.e. at /projectnb/<your_project_name>/<your_user_name>/conda_envs.
2. Open either your .bashrc, .bash_profile, or .profile file, whichever you already have in your home directory (you may have to select View > Show Hidden Files to see these files), and add the following lines anywhere, filling in the location of the folder you just created:

module load miniconda/4.7.5
module load openmpi/3.1.4
export PATH=$PATH:/projectnb/<your_project_name>/<your_user_name>/conda_envs

3. Now open up a NEW Terminal window.
4. Follow steps 3-5 from the previous section (including installing the Linux library).
5. Instead of just installing NEURON with pip install neuron, instead run pip install neuron netpyne to also install NetPyNE.
6. cd to the netpyne-batch-running subfolder of this code.
7. Follow step 8 from the previous section.
8. Now you can run the batch job submission and simulation by running python netpyne_batch.py! After the jobs complete, you can run python netpyne_analysis.py to compare the different simulations!

Instructions (Poetry)

If you want to use the excellent Poetry for your package management instead of conda, install Poetry and the version of Python you want to use, go to the directory where you downloaded this model, and run in the terminal

poetry install

followed by

poetry shell

to activate your environment, after which point you should be able to run all the simulations mentioned above.