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
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py
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run_test
saved_sim_makers
spike_output
stimulation
synaptic_activity
tdt2mat_data
tmp
VecStim
readme.html
readme.louise
readme.NSG
readme.specialcase.txt
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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
                            
// tdt2mat_data.hoc
// creates tdt2mat_data files that can be read by matlab to allow the simulation to 
// be analysed like the experiments
/* excerpts from diagnostic_script_5.m
tdt2mat_data.streams.BRTH.data = BRTH_data;
tdt2mat_data.streams.BRTH.fs = fs;
tdt2mat_data.snips.eNeu.ts=spike_times;
tdt2mat_data.snips.eNeu.sortcode=ones(1, length(spike_times)); % arbitrarily assign a sortcode of 1 for each spike time
tdt2mat_data.epocs.SOFF.onset = SOFF;
duplicates_S_ON = kron(S_ON, [1 1]);
tdt2mat_data.epocs.S_ON.onset=duplicates_S_ON;
stimids=1:length(S_ON);  % arbitrarily assign whole numbers as stimids
duplicates_stimids = kron(stimids, [1 1]);
tdt2mat_data.epocs.S_ON.data = duplicates_stimids;

----
use the following filenames where _d_ means _dot_ i.e. is a "." (see above)

tdt2mat_data_d_streams_d_BRTH_d_data.dat
tdt2mat_data_d_streams_d_BRTH_d_fs.dat
tdt2mat_data_d_snips_d_eNeu_d_ts.dat
tdt2mat_data_d_snips_d_eNeu_d_sortcode.dat
tdt2mat_data_d_epocs_d_SOFF_d.dat
tdt2mat_data_d_epocs_d_S_ON_d.dat
tdt2mat_data_d_epocs_d_S_ON_d_data.dat

*/

// first BRTH_data
// Make a sine wave lined up with the breath pulses:
objref BRTH_data, fs_vec, ts_vec, sortcode_vec, SOFF_vec, S_ON_vec, S_ON_data_vec
strdef tmpfilename // used by NSG to find run_X/tdt2mat_data for each simulation
proc save_tank() {
  breath_zero_time = 0 // ThetaStim[0].outer_start+ThetaStim[0].start // arbitrarily choose the start time of the breath pulses as zero
  // in the breath cycle
  breath_period = breathing_period // ThetaStim[0].outer_interval
  // assume that t_vec already exists and is the recorded time for the entire simulation

// comment in when want the big breath trace otherwise use shared breath trace to save space
//  BRTH_data=t_vec.c.sin(1/(breath_period/1000),PI/2-2*PI*breath_zero_time/breath_period, dt) // freq in Hz
  // uncomment the below if want to write the big file:
  ///// BRTH_data=t_vec.append(0) // store one number just to make a file
  ///// BRTH_data.c.mul(20).line(v_graph,t_vec,5,1)
  ///// sprint(tmpfilename, "%s_d_streams_d_BRTH_d_data.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  // skip writing these big identical files:
  // write_vec(tmpfilename, BRTH_data)

  fs_vec = new Vector()
  fs_vec.append(1000/dt) // if dt is 0.025 us this will be 40k Hz
  sprint(tmpfilename, "%s_d_streams_d_BRTH_d_fs.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename,fs_vec)
/////////////////////////////////////////
// augmented section for more possible activity traces

// ***************************** variants - this one is the original m1 activity
  // ts_vec is a vector of the time's of the spikes: this comes in NEURON in ms so needs conversion to seconds
  ts_vec=m1_events.c.div(1000) // 
  sprint(tmpfilename, "%s_d_snips_d_eNeu_d_ts_m1.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, ts_vec)

// ***************************** variants - this one is the pg reciprocal synapse - will try this first as main pg activity indicator
  // ts_vec is a vector of the time's of the spikes: this comes in NEURON in ms so needs conversion to seconds
  ts_vec=pg1_to_m1tuft_events.c.div(1000) // 
  sprint(tmpfilename, "%s_d_snips_d_eNeu_d_ts.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, ts_vec)

// ***************************** variants - this one is the pg1 axon to m2 priden and should match the recip synapse above
  // ts_vec is a vector of the time's of the spikes: this comes in NEURON in ms so needs conversion to seconds
  ts_vec=pg1_axon_to_m2_events.c.div(1000) // 
  sprint(tmpfilename, "%s_d_snips_d_eNeu_d_ts_pg1_to_m2.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, ts_vec)

// ***************************** variants - this one recorded the pg2 to m1 priden
  // ts_vec is a vector of the time's of the spikes: this comes in NEURON in ms so needs conversion to seconds
  ts_vec=pg2_axon_to_m1priden_events.c.div(1000) // 
  sprint(tmpfilename, "%s_d_snips_d_eNeu_d_ts_pg2_to_m1.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, ts_vec)

//////////////////////////////////////////////

  sortcode_vec = ts_vec.c // make a vector the same length as the vector of spikes
  sortcode_vec.fill(1) // make the sortcode arbitrarily set to 1
  sprint(tmpfilename, "%s_d_snips_d_eNeu_d_sortcode.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, sortcode_vec)

  SOFF_vec = new Vector()
  S_ON_vec = new Vector()
  S_ON_data_vec = new Vector()
/*
  num_of_points = light1_events.size()
  points_per_burst = ThetaStim[2].number // for light stimulation of mitral 1
  stimid=1
  for (i=0; i<num_of_points; i = i+ points_per_burst) {
    // note that the S_ON and SOFF times are duplicated because the analysis code expects them to be duplicated

    S_ON_vec.append(light1_events.x[i], light1_events.x[i])
    SOFF_vec.append(light1_events.x[i+points_per_burst-1], light1_events.x[i+points_per_burst-1])
    S_ON_data_vec.append(stimid, stimid)

    stimid = stimid + 1
  }
*/
  // recreate the S_ON, SOFF vectors from the gauss light parameters
  stimid = 1
  current_S_ON_time = light_gauss_center - light_half_width
  current_SOFF_time = light_gauss_center + light_half_width
  while (current_S_ON_time < tstop) { // keep going until past tstop
    // note that the S_ON and SOFF times are duplicated because the analysis code expects them to be duplicated
    if (current_S_ON_time>0) {
      S_ON_vec.append(current_S_ON_time, current_S_ON_time)
    }
    if (current_SOFF_time<tstop) {

      SOFF_vec.append(current_SOFF_time, current_SOFF_time)
    }
    S_ON_data_vec.append(stimid, stimid)
    // increment variables for next stimulus
    current_S_ON_time += light_period
    current_SOFF_time += light_period
    stimid = stimid + 1
  }

  sprint(tmpfilename, "%s_d_epocs_d_SOFF_d_onset.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, SOFF_vec.c.div(1000)) // divide by 1000 to convert from ms to seconds

  sprint(tmpfilename, "%s_d_epocs_d_S_ON_d_onset.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, S_ON_vec.c.div(1000))

  sprint(tmpfilename, "%s_d_epocs_d_S_ON_d_data.dat", tank_folder) // looks like run_X/tdt2mat_data/_d_...
  write_vec(tmpfilename, S_ON_data_vec)
/**/
}