Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)

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Accession:135903
"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. ..."
Reference:
1 . Stacey WC, Krieger A, Litt B (2011) Network recruitment to coherent oscillations in a hippocampal computer model. J Neurophysiol 105:1464-81 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA1 interneuron oriens alveus GABA cell; Hippocampus CA1 basket cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Oscillations;
Implementer(s): Lazarewicz, Maciej [mlazarew at gmu.edu]; Stacey, William [wstacey at med.umich.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA1 interneuron oriens alveus GABA cell; GabaA; AMPA; NMDA; I Na,t; I A; I K; I h;

strdef cmd
numiters =2
objref pr, pn[numiters]


//gapstyle=14 //now with high .01 pyr-pyr gaps 0-81, 1-82, 2-83 ... 19-100
gapstyle=15 //now with low .001 gaps


pyrthr=100  //this is the threshold at which pyramidal cells will have a noise event at each 0.5 ms
	//  the TGnet.tem will compare if a uniformly distributed number from 0-100 is > this threshold
basketthr=100   //noise threshold for basket cells
basktopyrgmax=5.5   //not used
pyrgapgmax =0.005   //not used
sigfreq=16    //frequency in Hz of the input signal
realrunFlag=1
singlerunFlag=0
noiserunFlag=0
Tstop   = 1600   //how many ms long this simulation will run

driverthr=80   //this is the threshold for noise input to the Drivers
//the four levels tested were:  55 (95 Hz), 65 (75 hz), 80 (50 hz), 85 (40 Hz)
//this number is held constant, and saved in the filename as "_f##"

{load_file("nrngui.hoc") }
{load_file("stdrun.hoc")}
{load_file("stdlib.hoc")} 
{load_file("netparmpi.hoc")}  //this gets loaded, but it was never run in parallel--several functions are not
// parallel-safe
{load_file("./templates/recruitnet.tem")}  //this creates the network


proc init() {  

	{pr.setScatteredVoltages(-85, -60)} //randomly starts each cell at different start voltages
  	finitialize() 
  	finitialize() 
}




celsius = 34
iteration=0
{cvode.active(0)}

proc DoRun() { localobj fo, fo1
{pr  = new Recruitnet()}

{pr.recordVoltages()}
if (noiserunFlag) {pr.recordnoise()}  //this is to record just the response of the noise, not the whole net

{pr.pnm.set_maxstep(0.01)}

{pr.pnm.want_all_spikes()}

runningTime = startsw()

stdinit()



{pr.activeSynapsesRandom(Tstop, pyrthr, basketthr, driverthr)}  // pyr threshold, bask threshold, driving pyr thresh
//that function creates all the noise events

{pr.pnm.psolve(Tstop)}  //solve the simulation


//below is a function to save files when I am recording from just one cell
//or check if this is a full "realrun" with the whole network
//or else just save a generic data file
	if (singlerunFlag) {pr.writesingleVoltage(basketthr,pyrthr,sigfreq)
		} else if (realrunFlag) {pr.writeVoltages(basketthr,pyrthr,gapstyle,driverthr)
		} else {pr.writeVoltages()}


//now check if it is also recording the noise sources
	if (noiserunFlag) {pr.writenoise(pyrthr)}


	{pr.pnm.pc.runworker()}   //this was not doing anything at present
	runningTime = startsw() - runningTime
	iteration=iteration+1
	print "Running Time: ", runningTime, "iteration: ",iteration

//now will save a spike raster
	{pr.pnm.gatherspikes()}
	fo = new File()
	fo1= new File()
	sprint(cmd, "data/spikes.dat")
	{fo1.wopen(cmd)}
	sprint(cmd, "data/spikes_b%d_p%d_g%d_f%d.dat",basketthr,pyrthr,gapstyle,driverthr)
	if (realrunFlag) {fo.wopen(cmd)}

   	for i=0, pr.pnm.spikevec.size-1 {
	if (realrunFlag) fo.printf("%-10.6lf, %d\n", pr.pnm.spikevec.x[i], pr.pnm.idvec.x[i])
		fo1.printf("%-10.6lf, %d\n", pr.pnm.spikevec.x[i], pr.pnm.idvec.x[i])
		}

 
if (realrunFlag) {fo.close()}
{fo1.close()}

{pr.pnm.pc.done()}

// now will save a history file for later use, to keep track of all files done
if (realrunFlag){
	{fo=new File()}
	{sprint(cmd, "spikes_b%d_p%d_g%d_f%d.dat",basketthr,pyrthr,gapstyle,driverthr)}
	{fo.aopen("data/spikelog.dat")}
	{fo.printf("%s\n",cmd)}
	{fo.close()}
	{fo=new File()}
	{sprint(cmd, "sum_b%d_p%d_g%d_f%d.dat",basketthr,pyrthr,gapstyle,driverthr)}
	{fo.aopen("data/sumlog.dat")}
	{fo.printf("%s\n",cmd)}
	{fo.close()}
	}

if (singlerunFlag){
	{fo=new File()}
	{sprint(cmd, "1_b%d_p%d_single_f%d.dat",basketthr,pyrthr,sigfreq)}
	{fo.aopen("data/singlelog.dat")}
	{fo.printf("%s\n",cmd)}
	{fo.close()}
	{fo=new File()}
	}

if (noiserunFlag){
	{fo=new File()}
	{sprint(cmd, "noise_b%d.dat",pyrthr)}
	{fo.aopen("data/noiselog.dat")}
	{fo.printf("%s\n",cmd)}
	{fo.close()}
	{fo=new File()}
	}


{pr.pnm.pc.gid_clear()}   //need to clear GIDs to reset the system
}

//this is how I do multiple runs.  
//from other testing I have determined that pyrthr 95 yields input noise of 0.008 nA2, 85 is 0.06 nA2, and 75 is 0.11 nA2
sigfreq=0
basketthr=100

driverthr=85   //40 Hz Drivers
pyrthr=92      //0.02 nA2 Neighbors
DoRun()
driverthr=65  //75 Hz Drivers
pyrthr=70	   //0.15 nA2 Neighbors
DoRun()
driverthr=55  //95 hz Drivers
pyrthr=85	  //0.06 nA2 Neighbors
DoRun()
return() //just makes an error to cancel the program

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