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]
Citations  Citation Browser
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
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 *
Ih.mod *
kamt.mod *
KCa.mod *
kdrmt.mod *
kfasttab.mod *
kM.mod *
KS.mod *
kslowtab.mod *
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
                            
// build_pg_net.hoc
// builds simple model
// OSN input goes to two mitral cells
// which are connected to a peri-glomerular cell

load_file("mct_cells.hoc") // loads the McTavish cell templates Mitral and Granule
load_file("PG_def.hoc") // loads the Li and Cleland 2013 pg cell template
objref m1, m2, pg
m1 = new Mitral()
// m2 = new Mitral()
pg = new PGcell(0) // the number 0 passed to PGcell() is the nicot. current

// OSNXs will be representative of breathing while LightXs are repr. of light stim.
objref OSN1, OSN2, Light1, Light2

// let these be NetStims for now.  Located arbitrarily because where they have an
// effect is determined by the NetCon.

m1.tuftden OSN1 = new ThetaStim(0.5)
m1.tuftden OSN2 = new ThetaStim(0.5)

m1.tuftden Light1 = new ThetaStim(0.5)
m1.tuftden Light2 = new ThetaStim(0.5)


// introduce synapses so they can be targets in NetCons:
objref m1_osn_glut, m2_osn_glut // excitation of mitral tufts by osn cells
objref pg_osn_glut, pg_osn_glut1 // excitation of pg cell from osn cells. glut1 for osn2

objref m1_pg_inhib, m2_pg_inhib // inhibition of mitral dends by pg
objref pg_m1_glut, pg_m2_glut // excitation of pg by mitral cells

// note m2 will be studied later - for now just sits here unconnected

tuft_excite_pos = 0.5
m1.tuftden m1_osn_glut = new AmpaNmda(tuft_excite_pos)
pg.gemmbody pg_osn_glut = new AmpaNmda(tuft_excite_pos)
pg.gemmbody1 pg_osn_glut1 = new AmpaNmda(tuft_excite_pos)
//m2.tuftden m2_osn_glut = new AmpaNmda(tuft_excite_pos)

mc_recip_pos = 0.05 // (0.05=1/20 of the sections length) with pg's represents 10 ums
// from the junction with the primary dendrite (the begining of the tuft dendrite)
m1.tuftden m1_pg_inhib = new FastInhib(mc_recip_pos) // start out with presumably effective 
//m2.tuftden m2_pg_inhib = new FastInhib(mc_recip_pos) // synapses near the mitral cell soma

pg_recip_pos1 = 0.55
pg_recip_pos2 = 0.65
pg.gemmbody pg_m1_glut = new AmpaNmda(pg_recip_pos1)
// when other mc is connected  pg.gemmbody pg_m2_glut = new AmpaNmda(pg_recip_pos2)

/////////////////////////////////////////////////////
//
//  connect the network
//
/////////////////////////////////////////////////////

// Connect the ThetaStims (OSN's) to the mc's

objref nc[8]
objref nclist
nclist = new List()

// connect the OSNs to the mcs

nc[0] = new NetCon(OSN1, m1_osn_glut, 0, 1, 1)  // arguments are source, target, threshold, delay, weight
// nc[1] = new NetCon(OSN2, m2_osn_glut)
nc[1] = new NetCon(OSN2, pg_osn_glut1)
// connect the Lights to the mcs

nc[6] = new NetCon(Light1, m1_osn_glut, 0, 1, 1)  // arguments are source, target, threshold, delay, weight
// nc[7] = new NetCon(Light2, m2_osn_glut)
nc[7] = new NetCon(Light2, pg_osn_glut1)

// connect the reciprocal synapse between m1 and pg

m1.secden[0] {nc[2] = new NetCon(&v(mc_recip_pos), pg_m1_glut, -20, 1, 1)}
pg.gemmbody nc[3] = new NetCon(&v(pg_recip_pos1), m1_pg_inhib)
// connect the reciprocal synapse between m2 and pg

// m2.secden[0] nc[4] = new NetCon(&v(mc_recip_pos), pg_m2_glut)
// pg.priden2[0] nc[5] = new NetCon(&v(pg_recip_pos2), m2_pg_inhib)

for i=0,5 {
  nclist.append(nc[i])
}

/////////////////////////////////////////////////////
//
// Adjust plasticity of FastInhib and AmpaNmda
//
/////////////////////////////////////////////////////

// it was decided the easiest thing to do was turn off
// plasticity in the AmpaNmda and FastInhib mod files
/*
// test section
objref test_pg
m1.tuftden test_pg = new ThetaStim(0.5) // stimulate granule cell synapse directly
objref test_nc
test_nc = new NetCon(test_pg, pg_m1_glut)

objref test_pg2
m1.tuftden test_pg2 = new ThetaStim(0.5) // stimulate granule cell synapse directly
objref test_nc2
test_nc2 = new NetCon(test_pg2, pg_m1_glut)

nclist.append(test_nc)
nclist.append(test_nc2)

// end test section
*/
/////////////////////////////////////////////////////
//
// Graphical control of ThetaStims
//
/////////////////////////////////////////////////////
objref hbox
hbox = new HBox()
hbox.intercept(1)

xpanel("ThetaStim[0] and [2]")
xlabel("OSN breathing input to mitral cell 1:")
xvalue("ThetaStim[0].outer_interval")
xvalue("ThetaStim[0].outer_start")
xvalue("ThetaStim[0].outer_number")
xvalue("ThetaStim[0].outer_noise")
xvalue("ThetaStim[0].interval")
xvalue("ThetaStim[0].start")
xvalue("ThetaStim[0].number")
xvalue("ThetaStim[0].noise")
// assign some default values
ThetaStim[0].outer_interval=400
ThetaStim[0].outer_start=25
ThetaStim[0].outer_number=80
ThetaStim[0].outer_noise=0
ThetaStim[0].interval=25
ThetaStim[0].start=25
ThetaStim[0].number=3

xlabel("Light input to periglomerular cell:")
xvalue("ThetaStim[2].outer_interval")
xvalue("ThetaStim[2].outer_start")
xvalue("ThetaStim[2].outer_number")
xvalue("ThetaStim[2].outer_noise")
xvalue("ThetaStim[2].interval")
xvalue("ThetaStim[2].start")
xvalue("ThetaStim[2].number")
xvalue("ThetaStim[2].noise")
// assign some default values
ThetaStim[2].outer_interval=395
ThetaStim[2].outer_start=25
ThetaStim[2].outer_number=80
ThetaStim[2].outer_noise=0
ThetaStim[2].interval=25
ThetaStim[2].start=25
ThetaStim[2].number=3

xpanel()
xpanel("ThetaStim[1] and [3]")
xlabel("OSN breathing input to periglomerular cell:")
xvalue("ThetaStim[1].outer_interval")
xvalue("ThetaStim[1].outer_start")
xvalue("ThetaStim[1].outer_number")
xvalue("ThetaStim[1].outer_noise")
xvalue("ThetaStim[1].interval")
xvalue("ThetaStim[1].start")
xvalue("ThetaStim[1].number")
xvalue("ThetaStim[1].noise")
ThetaStim[1].outer_interval=400
ThetaStim[1].outer_start=80
ThetaStim[1].outer_number=5
ThetaStim[1].outer_noise=0
ThetaStim[1].interval=25
ThetaStim[1].start=25
ThetaStim[1].number=3

xlabel("Light input to periglomerular cell:")
xvalue("ThetaStim[3].outer_interval")
xvalue("ThetaStim[3].outer_start")
xvalue("ThetaStim[3].outer_number")
xvalue("ThetaStim[3].outer_noise")
xvalue("ThetaStim[3].interval")
xvalue("ThetaStim[3].start")
xvalue("ThetaStim[3].number")
xvalue("ThetaStim[3].noise")
// assign some default values
ThetaStim[3].outer_interval=0
ThetaStim[3].outer_start=0
ThetaStim[3].outer_number=0
ThetaStim[3].outer_noise=0
ThetaStim[3].interval=0
ThetaStim[3].start=0
ThetaStim[3].number=0

xpanel()
/*xlabel("test pg belo ")
xvalue("ThetaStim[2].interval")
xvalue("ThetaStim[2].start")
xvalue("ThetaStim[2].number")
xvalue("ThetaStim[2].noise")
xlabel("test pg2 belo ")
xvalue("ThetaStim[3].interval")
xvalue("ThetaStim[3].start")
xvalue("ThetaStim[3].number")
xvalue("ThetaStim[3].noise")
*/
global_weight=1

//xvalue("prompt", "variable" [, boolean_deflt, "action" [, boolean_canrun, boolean_usepointer]])
//xvalue("global_weight","global_weight",2,"readjust_weights()",1, 0)
xpanel("Synapse weights")
xlabel("Synapse weights")
xvalue("global_weight")
xbutton("readjust_weights()")
xlabel("OSN1  (ThetaStim[1])to m1:")
xvalue("nc[0].weight")
xlabel("OSN2 (ThetaStim[1]) to pg:")
xvalue("nc[1].weight")
xlabel("m1 to pg:")
xvalue("nc[2].weight")
xlabel("pg back to m1:")
xvalue("nc[3].weight")
// xlabel("m2 to pg")
// xvalue("nc[4].weight")
// xlabel("pg back to m2")
// xvalue("nc[5].weight")
xlabel(" press below to graph stimulations")
xbutton("light(green) breath1(orange) breath2(purple)","{regraph_stims()}")
xbutton("save some data files","write_selected_vecs()")
xbutton("save simulation to tank","save_tank()")
xpanel()
hbox.intercept(0)
hbox.map()
// test values:
ThetaStim[0].number=3
ThetaStim[1].number=3
/*
ThetaStim[2].number=20

ThetaStim[3].number=50

ThetaStim[3].start = 330
*/

/////////////////////////////////////////////////////
//
// Setup vector and event recording for graphing/analysis
//
/////////////////////////////////////////////////////
objref t_vec, m1_v_vec, m2_v_vec

t_vec = new Vector()
m1_v_vec = new Vector()
//m2_v_vec = new Vector()

t_vec.record(&t)
m1_v_vec.record(&m1.soma.v(0.5))
//m2_v_vec.record(&m2.soma.v(0.5))

objref light1_events, light2_events
objref OSN1_events, OSN2_events, m1_events, m2_events, pg_events1, pg_events2

OSN1_events = new Vector()
OSN2_events = new Vector()
m1_events = new Vector()
//m2_events = new Vector()
pg_events1 = new Vector()
pg_events2 = new Vector()
light1_events = new Vector()
light2_events = new Vector()

nc[0].record(OSN1_events)
nc[1].record(OSN2_events)
nc[2].record(m1_events)
nc[3].record(pg_events1) // source position pg_recip_pos1 on granule priden2[0]
//nc[4].record(m2_events)
// nc[5].record(pg_events2) // source position pg_recip_pos2 on granule priden2[0]
nc[6].record(light1_events)
nc[7].record(light2_events) //for these connects the events are recorded into vectors here


//  activate all the synapses
proc readjust_weights() {
  for i=0,nclist.count-1 {
    nc[i].weight=global_weight
  }
}

readjust_weights()
// test_nc.weight=global_weight
// test_nc2.weight=global_weight

load_file("cells_volt_graphs_pg.ses")
load_file("run_cntrl.ses")
load_file("graph_fncs_pg.hoc")
load_file("tdt2mat_data.hoc")