BCM-like synaptic plasticity with conductance-based models (Narayanan Johnston, 2010)

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Accession:147538
" ... Although the BCM-like plasticity framework has been a useful formulation to understand synaptic plasticity and metaplasticity, a mechanism for the activity-dependent regulation of this modification threshold has remained an open question. In this simulation study based on CA1 pyramidal cells, we use a modification of the calcium-dependent hypothesis proposed elsewhere and show that a change in the hyperpolarization-activated, nonspecific-cation h current is capable of shifting the modification threshold. ..."
Reference:
1 . Narayanan R, Johnston D (2010) The h current is a candidate mechanism for regulating the sliding modification threshold in a BCM-like synaptic learning rule. J Neurophysiol 104:1020-33 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse; Channel/Receptor;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I A; I h; I Potassium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Synaptic Plasticity; Calcium dynamics;
Implementer(s): Narayanan, Rishikesh [rishi at iisc.ac.in];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I Na,t; I A; I h; I Potassium; Glutamate;
/* Implementation of BCM-like synaptic plasticity model in a
 * conductance-based model. The model contains Na, KDR, KA and h currents,
 * and employs GHK-based implementations of AMPA and NMDA receptors. A
 * calcium-based synaptic plasticity rule (from Shouval et al., PNAS, 2002)
 * is the incorporated into the model to update the weight w. All weight
 * updates are  performed in the MOD file "Wghkampa.mod". This file helps
 * recreate Fig. 2A and Fig. 2B of the following paper:
 
 * Narayanan R, Johnston D. The h current is a candidate mechanism for 
 * regulating the sliding modification threshold in a BCM-like synaptic 
 * learning rule.  J Neurophysiol. 2010 Aug;104(2):1020-33. doi: 
 * 10.1152/jn.01129.2009.  PubMed PMID: 20554832. PubMed Central PMCID: 
 * PMC2934916.
 
 * Implemented by Rishikesh Narayanan. Contact rishi.n@gmail.com.
 */



create dend
access dend


// All parameters are set as if this is an apical dendritic 
// section sitting around 200um from the soma.

objref cvode
cvode = new CVode()
cvode.active(1)

objref ampa, nmda, s
objref ncl, weight

double isilist[1]

//**************************************************************//
// The Parameters
//**************************************************************//

tstop = 30000
steps_per_ms = 40
dt = 0.025

// --------------------------------------------------------------
// Resistances and Capacitances
// --------------------------------------------------------------

ra     = 150			

rm     = 28000
rmdend = rm/2

c_m       = 0.75
cmdend    = c_m*2

v_init    = -65
celsius   = 34

// --------------------------------------------------------------
// Equilibrium potentials
// --------------------------------------------------------------

Eh=-30
Ek = -90
Ena = 55

//--------------------------------------------------------------
// Active conductance densities and other related parameters
//--------------------------------------------------------------

gna=0.03
nash=10
gkdr=0.005
gka=0.044		// Base value of gka=0.022*(1+xdist/100)
gh=0.0042		// Base value of gh=0.0006*(1+3*xdist/100)

//--------------------------------------------------------------
// NetStim parameters
//--------------------------------------------------------------

tsyn=20
intrvl=10
nmbr=900
noisevar=0.0

//--------------------------------------------------------------
// Synapse parameters
//--------------------------------------------------------------

P=1e-6	// AMPA Permeability in cm/s.
NAR=1.5	// Poirazi et al., 2003 use NAR of 0.6 for Trunk and 2.5 for
		// non trunk - this is somewhere in between!
w=0.25

//--------------------------------------------------------------
// The code.
//--------------------------------------------------------------

dend {
	L=50	// Take a 50um dendrite
	diam=1	// It is 1um thick
}

// Insert passive elements.

insert pas
e_pas = v_init
Ra=ra
cm=cmdend
g_pas=1/rmdend

// Insert active elements.

dend {
	insert nas gbar_nas=gna	ar_nas=0.8
	insert kdr gkdrbar_kdr=gkdr
	insert kad gkabar_kad=0
	insert hd 
	insert cad

	ghdbar_hd = gh 
	sh_nas=nash-4 // xdist=200; nash-8*(xdist-100)/200
	vhalfl_hd=-73-2 //xdist=200; -73-4*(xdist-100)/200
	gkabar_kad = gka
	
	ek = Ek
	ena = Ena
	ehd_hd=Eh

	finitialize(v_init)
    fcurrent()

	for (x) {
		e_pas(x)=v(x)
		e_pas(x)=e_pas(x)+(ina(x)+ik(x)+i_hd(x))/g_pas(x)
	}
}

s = new NetStim(0.5)
s.interval=intrvl   // ms (mean) time between spikes
s.number=nmbr     // (average) number of spikes
s.start=tsyn   // ms (mean) start time of first spike
s.noise=0      // range 0 to 1. Fractional randomness.

ampa=new Wghkampa(0.5)
ncl=new List()
dend ncl.append(new NetCon(s, ampa, 0, 0, 0))
ampa.taur		= 2	
ampa.taud		= 10	
ampa.Pmax		= P	
ampa.winit		= w

nmda=new ghknmda(0.5)
dend ncl.append(new NetCon(s, nmda, 0, 0, 0))
nmda.taur		= 5	
nmda.taud		= 50	
nmda.Pmax		= P*NAR

/********************************************************************/

proc update_Gh() { 
	dend{
		ghdbar_hd = gh
	}	
	update_init()
}

/********************************************************************/

proc update_init(){
	finitialize(v_init)
	fcurrent()
	for (x) {
		e_pas(x)=v(x)
		e_pas(x)=e_pas(x)+(ina(x)+ik(x)+i_hd(x))/g_pas(x)
	}

}
/********************************************************************/
// This function generates the % change in synaptic weight as a 
// function of induction frequency for various values of gh
// (Similar to Fig. 2A of the paper). The output is saved in a file named
// as "Fig2A_Output.txt". For each value of gh (which is printed first in
// the file), there are two columns of output: the induction frequency
// used and the % change in synaptic weight for that induction frequency.An
// example output, as generated by this function, is also attached with the
// same filename "Fig2A_Output.txt".

proc Fig2A() {
	gh=0.0042		
	weight= new Vector()
	double isilist[5000]
	isicount=0
	for (i=0.5;i<25.5; i+=0.5) {
		isilist[isicount]=1000/i
		isicount=isicount+1
	}
	
	ampa.winit=0.25
	w=0.25
	wopen("Fig2A_Output.txt")

	i_init=0

	for (gh=0.00; gh<=0.064; gh*=2) {	// For each value of Ih density
		fprint ("%f\n", gh)
		print gh
		update_Gh()
		for (i=i_init; i<isicount; i+=1) {	// For each ISI in the list
			s.interval=isilist[i]
			weight.record(&ampa.w)
			update_init()
			tstop=s.interval*900 // 900 pulses of that ISI
			while (t < tstop){
				fadvance()
			}	
			sum=0
			right=weight.size()-1000
			if (right<0) right=0
			count=0
			for (j=weight.size()-1; j>right; j-=1){ 
				sum=sum+weight.x[j]
				count=count+1
			}	
			sum=sum/count
			fprint("%f\t%f\n",1000/isilist[i],100*(sum/w-1))
			print 1000/isilist[i], "\t", sum, "\t", 100*(sum/w-1)
		}	
		if (gh==0.0) {
			gh=0.00025
		} 
	}	
	wopen()
}	

/********************************************************************/
// Computes the sliding modification threshold for each value of gh and
// saves in a file called "Fig2B_Output.txt". The output file has two
// columns: the first is the gh, and the second is the sliding modification
// threshold. An example output, as generated by this function, is also
// attached with the same filename "Fig2B_Output.txt".

proc Fig2B() {
	weight= new Vector()
	double isilist[1000]
	isicount=0
	for (i=0.2;i<25; i+=0.05) {
		isilist[isicount]=1000/i
		isicount=isicount+1
	}
	ampa.winit=0.25
	w=0.25
	wopen("Fig2B_Output.txt")
	i_init=0
	for (gh=0.0; gh<=0.012; gh+=0.00025) {	// For each value of Ih density
		print gh
		update_Gh()
		for (i=i_init; i<isicount; i+=1) {	// For each ISI in the list
			s.interval=isilist[i]
			weight.record(&ampa.w)
			update_init()
			tstop=s.interval*900 // 900 pulses of that ISI
			while (t < tstop){
				fadvance()
			}	
			sum=0
			right=weight.size()-1000
			if (right<0) right=0
			count=0
			for (j=weight.size()-1; j>right; j-=1){ 
				sum=sum+weight.x[j]
				count=count+1
			}	
			sum=sum/count
			print 1000/isilist[i], "\t", sum, "\t", sum/w

			if (sum/w > 1){
				fprint ("%f\t%f\n", gh, 1000/isilist[i])
				break // Break if LTP threshold has been reached.
			}		

// Increasing gh shifts the sliding modification threshold to the right, so
// the following step to have i_init to start from 10 steps behind the
// current i is a time-saving exercise, so that i_init does not have to
// start from 0 always. 

			i_init=i-10 

			if (i_init<0) i_init=0
		}	
	}	
	wopen()
}	

/********************************************************************/
// Run Fig2A () for obtaining data for Fig. 2A of the paper.
// Run Fig2B () for obtaining data for Fig. 2B of the paper.

xpanel("Fig 2")
xbutton("Run Fig2A", "Fig2A()")
xbutton("Run Fig2B", "Fig2B()")
xlabel("Select one of the two simulations above")
xpanel()

//Fig2A()
//Fig2B() 

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