Locust olfactory network with GGN and full KC population in the mushroom body (Ray et al 2020)

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Accession:262670
We reconstructed the GGN morphology from 3D confocal image stack, and built a passive model based on the morphology to study signal attenuation across this giant neuron. In order to study the effect of feedback inhibition from this cell on odor information processing, we created a model of the olfactory network in the locust mushroom body with 50,000 KCs reciprocally connected to this neuron. Finally, we added a model of the IG to reproduce in vivo odor responses in GGN.
Reference:
1 . Ray S, Aldworth ZN, Stopfer MA (2020) Feedback inhibition and its control in an insect olfactory circuit. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism: Mushroom body; Locust;
Cell Type(s): Honeybee kenyon cell; Abstract Izhikevich neuron; Locust Giant GABAergic Neuron (GGN);
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Gaba; Acetylcholine;
Simulation Environment: NEURON;
Model Concept(s): Olfaction; Sensory processing;
Implementer(s): Ray, Subhasis [ray.subhasis at gmail.com];
Search NeuronDB for information about:  Acetylcholine; Gaba;
/
mbnet
analysis
common
mb
morphutils
nrn
README.md
                            
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` : morphology trace of GGN shrinkage corrected for
	methylsalicylate fixing
-   `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

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