Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)

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Accession:183300
To explore interactions between respiration, inhibition, and olfaction, experiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico. This archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses.
Reference:
1 . Short SM, Morse TM, McTavish TS, Shepherd GM, Verhagen JV (2016) Respiration Gates Sensory Input Responses in the Mitral Cell Layer of the Olfactory Bulb. PLoS One 11:e0168356 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell; Olfactory bulb main tufted cell external;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Sensory processing; Sensory coding; Bursting; Oscillations; Olfaction;
Implementer(s): Morse, Tom [Tom.Morse at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron periglomerular cell; Olfactory bulb main interneuron granule MC cell;
Files displayed below are from the implementation
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ShortEtAl2016
early_theta_version
event_generator
import
py
run_0
run_1
run_10
run_11
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run_13
run_14
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run_2
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run_8
run_9
run_test
saved_sim_makers
spike_output
stimulation
synaptic_activity
tdt2mat_data
tmp
VecStim
readme.html
readme.louise
readme.NSG
readme.specialcase.txt
ampanmda.mod *
cadecay.mod *
cadecay2.mod *
Caint.mod *
Can.mod *
CaPN.mod *
CaT.mod *
fi.mod
GradeAMPA.mod *
GradeGABA.mod *
GradNMDA.mod *
hpg.mod *
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kamt.mod *
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NaP.mod *
naxn.mod
Nicotin.mod *
nmdanet.mod *
OdorInput.mod *
thetastim.mod *
ThreshDetect.mod *
vecstim.mod *
batch_run_first_NSG.py
batch_runs.py
batch_runs.py20150708
batch_runs.py20150808gc_error
batch_runs_first_NSG.py
build_net.hoc
build_net_Shep.hoc
build_net_Shep_NSG.hoc
build_net_Shep_NSG20160825.hoc
build_net_SMS.hoc
build_net_theta.hoc
build_net20150312.hoc
build_pg_net.hoc
cell_properties_for_ET_from_standalone.txt
cells_volt_graphs.ses
cells_volt_graphs_pg.ses
create_arrays.py
documentation.txt
et.hoc
et_rig.ses
et_rig2.ses
Et_start.zip
granule.hoc *
graph_fncs.hoc
graph_fncs_pg.hoc
gui_stim.hoc
how_to_run_pre_init_on_mac.txt
inhib_study.eps
inhib_study.ps
init.hoc
init.py
make_lookup_table.sh
makelib.err
makelib.out
mct_cells.hoc
mitral.hoc
mosinit.hoc
nrnivmodl.out
num_of_columns.hoc
PG_def.hoc
pre_init.py
pre_init_first_NSG.py
pre_init_no_changes_in_weights.py
roberts_python_help.txt
run_on_serial.hoc
runcntrl.ses
sample_gc1_v_graph.ses
sample_mitral_pg_space_plots.ses
screenshot.png
screenshot0.png
tdt2mat_data.hoc
temporary_file.tmp
test_matplotlib.hoc
                            
This readme describes how to run the cluster code for simple circuits.

Overview:

A program called batch_runs.py will be the master program to run to
prepare many simulations to run at once. batch_runs.py executes a file
called create_arrays.py that sets breath and light stimulus lists that
are then used to generate parameter settings for each
simulation. run_X folders are created and the contents of a template
model folder called simple_circuits are copied to each of these
folders along with the unique parameters.hoc files created by
batch_runs.py.  Then the simulations are run with qsub.

Usage:

1) unzip this archive on a cluster with PBS (portable batch system) and NEURON installed

2) edit the create_arrays.py program to 

the desired pairs of breathing
(respiration) (B) and light evoked (stimulated (S) peak rates (ends up
in variables called breath_peak_rate, light1_peak_rate in the NEURON
code). These must be created in a "both" list, for example to combine 50, and 400 in
all possible ways both should look like:

both=[(50,50),(50,400),(400,50),(400,400)]

the first number in each tuple corresponds to B and the second to S.
The both list is the only variable reused from the create_arrays.py
program. Note that the peak rates refer to the number of OSN axon firing
events that produce EPSPs in the dendrites of the cells connected to OSNs (olfactory
sensory neurons).

3) edit batch_run.py for the following:

3a) change the assignment of absolute path to match the folder that
you will create the run_X's in.  For example if you are running in
/home/tmm46/project/VerhagenLab/20150714/batch_runs then assign
absolute_path = "/home/tmm46/project/VerhagenLab/20150714/batch_runs/"
where you include the last slash.

3b) edit, if desired, where it writes the
num_of_columns.hoc file for each run_X folder.  This gives the
possibility of simulations with different numbers of columns.

3c) edit the batch_runs.py to set the p[] (parameters.hoc file texts)
array.  p[0] is the string that represents the parameters.hoc file in
folder run_0, p[1] for run_1/parameters.hoc, etc..  Note that there
could be multiple settings of p[] for different types of network runs.

3d) make sure that the p[]'s include a net_type hoc comment like

// net_type pg_net

for example if the parameters correspond to a pg_net.  This is needed
because it was troublesome to automatically detect which type of
network was present and gives the possibility of new types of
networks.  This name is reported in subsequent analysis programs. It
is important to exactly have the begining part be "// net_type ".

5) run batch_runs.py with a command like
./batch_runs.py
(should work if you have python in /usr/bin/python and batch_runs.py
has been set to executable with a command like
chmod 775 batch_runs.py

if it is not already executable. This creates the run_X's, which
except for needing their mod files compiled are fully functional (can
run with the command nrngui build_net_Shep.hoc.

6) run the simulations with a command like
qsub run.pbs
where the pbs file was generated with a command like:
/usr/local/cluster/software/installation/SimpleQueue/sqPBS.py gen 8 tmm46 nrn_task tasklist > run.pbs
tmm46 can be replaced for example by sms296
Double check that you have the right number of processors and nodes for your job.

7) Collect the output results with a command like 
zip -r results.zip run_?/tdt2mat_data run_*/parameters.hoc

so that results retain which parameters were included.  If the model
was run with varying columns make sure that you also zip up num_of_columns.hoc
zip -r results.zip run_?/tdt2mat_data run_*/parameters.hoc run_*/num_of_columns.hoc




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