HB-PAC_disinhibitory_network

Description

This repository contains the code to simulate a neuronal network in the honeybee primary auditory center.

This model has been added to ModelDB (Accession number 239413)

This repo contains parts of the code written by Aynur Maksutov during AMGEN program 2016 at Wachtlerlab, LMU, Munich.

Author

Ajayrama Kumaraswamy, ajayramak@bio.lmu.de

References

Experimental context and brief summary

Ai, H., Kai, K., Kumaraswamy, A., Ikeno, H., & Wachtler, T. (2017). Interneurons in the honeybee primary auditory center responding to waggle dance-like vibration pulses. The Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.0044-17.2017

Detailed description of model and simulations

Kumaraswamy, A., Maksutov, A., Kai, K., Ai, H., Ikeno, H., & Wachtler, T. (2017). Network simulations of interneuron circuits in the honeybee primary auditory center. bioRxiv. https://doi.org/10.1101/159533

Installation

With anaconda (recommended):

Step 1: Download the repository

Step 2: Create a new virtual environment and install some dependencies

conda create --name Ai2017Sim -c brian-team ipython>=6.1 numpy>=1.11.2 matplotlib>=1.5.3 seaborn>=0.7.1 brian2>=2.0.1 python>=3.5

Step 3: Activate the virtual environment

source activate Ai2017Sim (unix) or activate Ai2017Sim (windows)

Step 4: Install the package (the option '-e' is required)

pip install -e <full path of this repository>

Without anaconda (normal python installation required, https://www.python.org/)

Step 1: Download the repository

Step 2: Install virtualenvwrapper (unix) or virtualenvwrapper-win (windows) with pip

Step 3: (only on windows) Install microsoft Visual C++ 14.0. Get it with "Microsoft Visual C++ Build Tools" here

Step 4: Create virtual environment

mkvirtualenv Ai2017Sim

Step 5: Install the package (the option '-e' is required)

pip install -e <full path of this repository>

Usage:

Step 1: Activate the virtual environment

source activate Ai2017Sim (unix) or activate Ai2017Sim (windows)

Step 2: Change the variable 'homeFolder' in the file 'dirDefs.py' to a folder of your choice. The results of the simulation will be stored here.

Step 3: The scripts of this repository are described below. All of them have some parameters at their top. Change these and run the scripts as needed.

Overview of Contents

• HB-PAC_disinhibitory_network

  • Ai2017Sim.yml: A file that can be used to create a conda environment to run the scripts below. Essentially is a list of dependencies.

  • models

    • neuronModels.py: wrapper classes for brian2 neuron models
    • neurons.py: Model equations and static parameters for neurons
      
    • synapses.py: Model equations for synapses
      
      

  • paramLists

    • AdExpPars.py: Parameter combinations for the AdExp model
      
    • inputParsList.py: Stimulii definitions
      
    • synapsePropsList.py: Parameter combinations for the difference of exponential synaptic conductance model
      
      

  • brianUtils.py: utility functions related to brian2

  • dirDefs.py: directory definitions imported in other scripts

  • DLInt1SynCurrent.py: Script to simulate DL-Int-1 recording membrane potential and synaptic currents in NIX files

  • DLInt2try.py: Legacy code

  • forAi2017.py: Script to generate a subplot of an upcoming manuscript.

  • JODLInt1DLInt2: Class to run network simulations

  • justDLInt1.py: Legacy code

  • mplPars.py: matplotlib rc parameters

  • neoNIXIO.py: adapted from GJEphys, utility functions to work jointly with NIX and neo.

  • plotDLInt1DLInt2SynEffects.py: script to plot summary of DL-Int-1 and DL-Int-2 responses to pulse trains.

  • plotShortStims.py: script to plot summary of DL-Int-1 and DL-Int-2 responses to short continuous pulses.

  • plotSynCurrents.py: script to plot membrane potential and synaptic currents of DL-Int-1 and DL-Int-2 for one stimulus.

  • runJODLInt1DLInt2Multiple.py: script to simulate the network for multiple stimulii. Output is saved as a NIX File.

  • simSynCurrents.py: script to simulate DL-Int-1 and DL-Int-2 recording membrane potential and synaptics currents in a NIX file.