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]
Citations  Citation Browser
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
data
Input
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
nrniv.exe.stackdump
OB.hoc
Parameter.hoc
PG_def.hoc
PG_save.hoc *
PG_Stim.hoc
SaveData.hoc
tabchannels.dat *
tabchannels.hoc
                            
TITLE Slow inactivating current Iks

COMMENT
     Slow inactivating current: Wang 1993 Neuroreport
ENDCOMMENT

:INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }

UNITS { 
	(mV) = (millivolt) 
	(mA) = (milliamp) 
} 

NEURON { 
	SUFFIX IKs
	USEION k READ ek WRITE ik
	RANGE gbar, ik
}

PARAMETER { 
	gbar =  0.0 	(mho/cm2)
	ek   = -70	(mV)
	k    =  1.5       : 1.5 !!!
	sha  =  0   (mV)
	shi  =  -3   (mV) : -3 !!!
	tauM = 10   (ms) : 10
} 

ASSIGNED { 
    v  (mV)
	ik  		(mA/cm2) 
	minf 		
	mtau 		(ms)
	hinf
	htau		(ms)
} 

STATE {
	m h
}

INITIAL { 
	Rates(v) 
	m = minf
	h = hinf
} 

BREAKPOINT { 
	SOLVE states METHOD cnexp
	ik = gbar * m * h * ( v - ek )
} 


DERIVATIVE states { 
	Rates(v) 
	m' = ( minf - m ) / mtau
	h' = ( hinf - h ) / htau 
}

UNITSOFF
 
PROCEDURE Rates(v) { 
	:TABLE minf, mtau, hinf, htau FROM -120 TO 40 WITH 641
	minf  = 1 / ( 1 + exp( -(v + 34 - sha) / 6.5 ) ) 
	mtau  = tauM 
	hinf = 1 / ( 1 + exp((v + 65 - shi) / 6.6 ))
	htau = 200 + k*220/( 1 + exp( -(v + 71.6) / 6.85 ))
}
UNITSON