A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:149739
This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.
Reference:
1 . Li G, Cleland TA (2013) A two-layer biophysical model of cholinergic neuromodulation in olfactory bulb. J Neurosci 33:3037-58 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,p; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I Potassium; I_Ks; I Cl, leak; I Ca,p;
Gap Junctions:
Receptor(s): Nicotinic; GabaA; Muscarinic; AMPA; NMDA;
Gene(s):
Transmitter(s): Acetylcholine;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Sensory processing; Sensory coding; Neuromodulation; Olfaction;
Implementer(s): Li, Guoshi [guoshi_li at med.unc.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; Nicotinic; GabaA; Muscarinic; AMPA; NMDA; I Na,p; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I Potassium; I_Ks; I Cl, leak; I Ca,p; Acetylcholine;
/
ACh_ModelDB
celldata
Connection
data
Input
SP
Readme.txt
cadecay.mod *
cadecay2.mod *
Caint.mod *
Can.mod *
CaPN.mod *
CaT.mod *
GradeAMPA.mod *
GradeGABA.mod *
GradNMDA.mod *
hpg.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 *
Background.hoc
Connect.hoc
GC_def.hoc
GC_save.hoc *
GC_Stim.hoc
Input.hoc
MC_def.hoc
MC_save.hoc
MC_Stim.hoc
mod_func.c
mosinit.hoc
OB.hoc
Parameter.hoc
PG_def.hoc
PG_save.hoc *
PG_Stim.hoc
SaveData.hoc
tabchannels.dat *
tabchannels.hoc
                            
// OB network model to simulate ACH modulation
// Guoshi Li, Cornell Univeristy 2013

// Simulation results are presented in the associated paper:
// Li and Cleland (2013) A two-layer biophysical model of cholinergic neuromodulation in olfactory bulb. 
// Journal of Neuroscience 33:3037–3058.

xopen("$(NEURONHOME)/lib/hoc/noload.hoc") // standard run tools
xopen("tabchannels.hoc")
nrncontrolmenu()

v_init  = -70     // Initialize the voltage
xpanel("Auto launch menu")
  xbutton("Default run 3000ms ", "default_run()")
  xbutton("Short demo 3 ms","demo_run()")
xpanel()

celsius = 35

dt  = 0.001       // The simulation step is set to 0.001 ms for results presented
				  // in the Journal of Neuroscience Paper (Li and Cleland 2013) 
				  // Simulation time is about 3.5 hours for one run in a single PC
                  // Small time step is used to ensure accuracy of results
				  // Larger step could be used for testing purpose
				  
secondorder  = 2  // Simulate using the Crank-Nicholson method


steps_per_ms = 5  // Points plotted per ms; for graphical display
DT  = 0.2         // Data recording interval: DT = 0.2 ms

load_file("Parameter.hoc")    // Contain parameters of the model
load_file("MC_def.hoc")       // Mitral cell template 
load_file("PG_def.hoc")       // PG cell template
load_file("GC_def.hoc")       // GC cell template      
load_file("Connect.hoc")      // Specify network connection
load_file("Background.hoc")   // Deliver random background inputs to the network
load_file("Input.hoc")        // Introduce afferent inputs to the model 
load_file("SaveData.hoc")     // Save simulation data for off-line analysis


proc advance() {
fadvance()
}

proc run() {
  running_ = 1
  stdinit()
  continuerun(tstop)
}

proc default_run() {
  tstop   = 3000    // Total simulation time: 3 sec
  run()
  save_data()
}


proc demo_run() {
  tstop   = 3    // Total simulation time: 0.003 sec
  run()
  save_data()
}



Loading data, please wait...