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]
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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 GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA 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 GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA 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
run_12
run_13
run_14
run_15
run_16
run_17
run_2
run_3
run_4
run_5
run_6
run_7
run_8
run_9
run_test
saved_sim_makers
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 *
KCa.mod *
kdrmt.mod *
kfasttab.mod *
kM.mod *
KS.mod *
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LCa.mod *
nafast.mod *
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
                            
import itertools
import numpy

# below creates symmetrical arrays
add_columns=[1]

B=[200] #range(20,780,20)# /1.0 # 0.0 20.0 ... 620.0
#(start, stop, step)


S=[60, 120, 180, 240, 320, 640] #range(20,780,20) #/1.0 # [50 50 ... 50 50]

both=list(itertools.product(B,S))

print both
# adding a columns array.  This is actually the number of additional columns
# in each case
#  add_columns = [1, 2, 4, 6]
add_columns = [1] # just one additional column

# create asymmetrical array where S<B
#
# go from B=0 to max_B
# for each B value include only S<B
#
#B=numpy.zeros(0,dtype=float,order='C')
#delta_S = 10 # for S
#delta_B = 60 # for B
#max_B = 300
#big_B = B
#big_S = B
#for new_B in range(delta_B, max_B+delta_B, delta_B):
#  for new_S in range(delta_S,new_B+delta_S,delta_S):
#    big_B=numpy.append(big_B,numpy.array([new_B]))
#    big_S=numpy.append(big_S,numpy.array([new_S]))

# override for special cases Shaina requested:

#big_B=[50, 50, 400, 400]
#big_S=[50, 400, 50, 400]
#big_B=[100, 100, 300, 300]
#big_S=[100, 300, 100, 300]

#both=zip(big_B,big_S) # order they appear in the parameter.hoc template