This directory contains scripts for simulating the GGN model and the mushroom body olfactory circuit around it in NEURON 7.4 with Python 2.7 related to the article:

"Feedback inhibition and its control in an insect olfactory circuit". Subhasis Ray, Zane Aldworth, and Mark Stopfer; 2020. eLife.

analysis

• various example scripts for analyzing simulated data.

common

• nhpp.py : Generate times for nonhomogeneous Poisson process

mb

cell_templates

• GGN_20170309_sc.swc : shrinkage corrected trace of GGN morphology.
• GGN_20170309_sc.hoc : cell template for GGN model
• kc_1_comp.hoc : single compartmental KC model

mod: contains mechanism files

network

• change_pn_spikes.py : take a simulated data file and create another one after modifying the PN spike times, so that same model is simulated with modified spike input.
• config.yaml : configuration file for setting network model parameters
• fixed_network_changing_stim.py : simulate a given network model with different PN spike inputs.
• kc_ggn_feedback_dclamp.py : test of single KC with GGN inhibition when the GGN is driven by a dynamic clamp.
• kc_ggn_feedback_frequency_sweep.py : amplitude and frequency sweeps for testing effect of GGN feedback on a single KC.
• kc_ggn_nofeedback.py : script to simulate KC with no feedback alongside KC with feedback inhibition from GGN.
• pn_kc_ggn_network.py : script to setup and simulate the mushroom body network model (uses config.yaml for parameters).
• pn_output.py : script to setup PN spike trains
• tweak_template.py : script to modify an existing network template (in a data file dumped by an earlier simulation).

slurm : utility scripts for running simulations in batch mode under slurm (on NIH biowulf).

• batch_run_remove_kcs_run.sh : example script for running successive simulations after removing high spiking KCs.
• circular_run : scripts for running running successive simulations after removing high spiking KCs. These scripts read last job ids from a specified file to identify the corresponding data dumps, remove high spiking kcs from those model templates, and run the simulation again until no more highspiking KC is left.
• run_fixed_net_changing_stim.py : example utility script to run simulation of a given network model with changed PN inputs as a subprocess.
• run_fixed_net_with_ig.sh : sample script to run a given network template including IG with different PN inputs.
• run_fixed_network_changing_stim.sh : script to run a fixed network template while changing the PN input pattern.
• run_kc_ggn_feedback_amp_sweep.sh, run_kc_ggn_nofeedback_amp_sweep.sh : scripts to test single KC with and without GGN feedback.
• run_mb_net.sh : script to run mushroom body network model in batch mode.
• run_to_reproduce_ggn_vm_no_seed.sh, run_to_reproduce_ggn_vm.sh, run_to_reproduce_ggn_vm_with_shifting_pn.sh: scripts that run multiple simulations while changing parameters to reproduce realistic GGN voltage trace.
• run_with_ig.sh : script to simulate network model with IG included.

test_cell

• ggn_voltage_attenuation_vclamp.py : check voltage attenuation along GGN arbor when one branch is voltage clamped.
• run_param_sweep_vclamp.py : test voltage attenuation along GGN arbor with voltage clamp while changing passive properties.

morphutils

• cellmovie.py : dump a video of rotating neuron in 3D
• displaycell.py : display neuronal morphology from SWC file
• morph3d_*.py : display neuronal morphology in 3D using corresponding module. Use vtk/vispy/matplotlib to display 3D morphology.
• neurograph.py : handle morphology as a graph. This file contains data type definitions and functions to read an SWC file and turn it into a graph (using networkx). There is also GGN specific mapping where I assign custom types to specific branches based on anatomical location.

nrn

• nrnutils.py : Utilities for handling NEURON model
  • convert a NEURON cell model into a networkx graph
  • insert alpha synapses
  • insert ion channel mechanisms
  • set up recording of Vm
• localized_input_output.py : apply synaptic inputs at specified branches. This scripts runs simulation with synchronous synaptic inputs at multiple compartments on specific branches.
• localized_input_output_passive_sweep.py : apply synaptic inputs at specified branches of GGN models with different passive properties.
• nrninit.bat, nrninit.sh : batch file (Windows) and shell script (Linux). Initialize PYTHONPATH to include various model related Python scripts
• staggered_input.py : simulate synaptic input at random compartments in specified branch