Hippocampus CA1 Interneuron Specific 3 (IS3) in vivo-like virtual NN simulations (Luo et al 2020)

 Download zip file 
Help downloading and running models
Accession:265523
"Disinhibition is a widespread circuit mechanism for information selection and transfer. In the hippocampus, disinhibition of principal cells is provided by the interneuron-specific interneurons that express the vasoactive intestinal polypeptide (VIP-IS) and innervate selectively inhibitory interneurons. By combining optophysiological experiments with computational models, we determined the impact of synaptic inputs onto the network state-dependent recruitment of VIP-IS cells. We found that VIP-IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, by integrating their intrinsic and synaptic properties into computational models, we predicted recruitment of these cells between the rising phase and peak of theta oscillation and during ripples. Two-photon Ca2+-imaging in awake mice supported in part the theoretical predictions, revealing a significant speed modulation of VIP-IS cells and their preferential albeit delayed recruitment during theta-run epochs, with estimated firing at the rising phase and peak of the theta cycle. However, it also uncovered that VIP-IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of VIP-IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding."
Reference:
1 . Luo X, Guet-McCreight A, Villette V, Francavilla R, Marino B, Chamberland S, Skinner FK, Topolnik L (2020) Synaptic Mechanisms Underlying the Network State-Dependent Recruitment of VIP-Expressing Interneurons in the CA1 Hippocampus. Cereb Cortex [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampal CA1 CR/VIP cell;
Channel(s): I Na,t; I Na,p; I A;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Spatial Navigation; Oscillations; Activity Patterns;
Implementer(s): Guet-McCreight, Alexandre [alexandre.guet.mccreight at mail.utoronto.ca];
Search NeuronDB for information about:  I Na,p; I Na,t; I A; Gaba; Glutamate;
/
LuoEtAl2020Code
Theta
SDprox1
NPYfiles
PLOTfiles
IKa.mod *
ingauss.mod *
Ksoma.mod *
Nap.mod *
Nasoma.mod *
vecevent.mod *
init.py *
IS3_M2_Case8StarRevised.hoc *
model_decaytimeinhvec.dat *
model_decaytimevec.dat *
model_dendsectionvec.dat *
model_distvec.dat *
model_minweightinhvec.dat *
model_minweightvec.dat *
model_risetimeinhvec.dat *
model_risetimevec.dat *
PlotResults.py
ranstream.hoc *
SynParamSearch.hoc
vecevent.hoc *
vecevent.ses *
                            
// This script is used to search the synaptic parameter space of the IS3 model by varying the number of excitatory and inhibitory synapses as well as their presynaptic spike rates

load_file("nrngui.hoc")
load_file("IS3_M2_Case8StarRevised.hoc") // Loads IS3 model with full morphology & properties (as well as parameters and point processes)

// Initialize theta synapses (precise number not needed so just 500 indices should be fine since this is more than the number of compartments in the model)
objectvar ExcThetaSRsyns[500], ExcThetaSLMsyns[500], ExcThetaSRsynsNCS[500], ExcThetaSLMsynsNCS[500], ExcThetaSRsynsNSS[500], ExcThetaSLMsynsNSS[500]
objectvar InhThetaSRsyns90[500],  InhThetaSRsyns180[500], InhThetaSRsyns270[500], InhThetaSLMsyns90[500],  InhThetaSLMsyns180[500], InhThetaSLMsyns270[500]
objectvar InhThetaSRsyns90NCS[500],  InhThetaSRsyns180NCS[500], InhThetaSRsyns270NCS[500], InhThetaSLMsyns90NCS[500],  InhThetaSLMsyns180NCS[500], InhThetaSLMsyns270NCS[500]
objectvar InhThetaSRsyns90NSS[500],  InhThetaSRsyns180NSS[500], InhThetaSRsyns270NSS[500], InhThetaSLMsyns90NSS[500],  InhThetaSLMsyns180NSS[500], InhThetaSLMsyns270NSS[500]

SRexcsyncount = 0
SLMexcsyncount = 0
inhsyncount = 0
thetaSRcount = 0
thetaSLMcount = 0
count = 0 // for indexing purposes to do with the input vectors
for (dendn = 0; dendn<=57; dendn = dendn+1){
	print "Section Number: ", dendn_vec.x[dendn]
			
	for (i = 1; i<=dend[dendn].nseg; i = i+1) {
		if (dendn > 17 && dendn < 23) { // Skip putting synapses on axonal segments
			count = count + 1
		 	break
		}
	
		// Specifies proportion along section (i.e. from 0 to 1)
		prop = ((dend[dendn].L/dend[dendn].nseg)*i - (dend[dendn].L/dend[dendn].nseg)/2)/dend[dendn].L // finds the center of each segment, as defined by its proportional distance along each section; (prop = (i-0.5)/dend[dendn].nseg also works)
			
		// Assign optimized synapse parameter values to 9 excitatory synapses on the compartment if in SR
		access dend[dendn]
		if (distance(prop)<=300) {
			for (l = 1; l<=9; l = l + 1){
				SRexcsynapses[SRexcsyncount] = new Exp2Syn(prop)
				dend[dendn] SRexcsynapses[SRexcsyncount].loc(prop) // assign to current compartment
				SRexcsynapses[SRexcsyncount].tau1 = 2.9936e-04
				SRexcsynapses[SRexcsyncount].tau2 = 2.4216
				SRexcsynapses[SRexcsyncount].e = 0
				SRexcnss[SRexcsyncount] = new VecStim(prop)
				SRexcncs[SRexcsyncount] = new NetCon(SRexcnss[SRexcsyncount], SRexcsynapses[SRexcsyncount])
				SRexcncs[SRexcsyncount].weight = 0.00000230814*distance(prop) + 0.00022016666
				SRexcsyncount = SRexcsyncount + 1
			}
			// THETA SYNAPSES
			ExcThetaSRsyns[thetaSRcount] = new Exp2Syn(prop)
			dend[dendn] ExcThetaSRsyns[thetaSRcount].loc(prop)
			ExcThetaSRsyns[thetaSRcount].tau1 = 2.9936e-04
			ExcThetaSRsyns[thetaSRcount].tau2 = 2.4216
			ExcThetaSRsyns[thetaSRcount].e = 0
			ExcThetaSRsynsNSS[thetaSRcount] = new NetStim(prop)
			ExcThetaSRsynsNCS[thetaSRcount] = new NetCon(ExcThetaSRsynsNSS[thetaSRcount], ExcThetaSRsyns[thetaSRcount])
			ExcThetaSRsynsNCS[thetaSRcount].weight = 0.00000230814*distance(prop) + 0.00022016666
						
			InhThetaSRsyns90[thetaSRcount] = new Exp2Syn(prop)
			dend[dendn] InhThetaSRsyns90[thetaSRcount].loc(prop)
			InhThetaSRsyns90[thetaSRcount].tau1 = 0.1013
			InhThetaSRsyns90[thetaSRcount].tau2 = 4.8216
			InhThetaSRsyns90[thetaSRcount].e = -70
			InhThetaSRsyns90NSS[thetaSRcount] = new NetStim(prop)
			InhThetaSRsyns90NCS[thetaSRcount] = new NetCon(InhThetaSRsyns90NSS[thetaSRcount], InhThetaSRsyns90[thetaSRcount])
			InhThetaSRsyns90NCS[thetaSRcount].weight = 0.00000469125*distance(prop) + 0.0002695779
			
			InhThetaSRsyns180[thetaSRcount] = new Exp2Syn(prop)
			dend[dendn] InhThetaSRsyns180[thetaSRcount].loc(prop)
			InhThetaSRsyns180[thetaSRcount].tau1 = 0.1013
			InhThetaSRsyns180[thetaSRcount].tau2 = 4.8216
			InhThetaSRsyns180[thetaSRcount].e = -70
			InhThetaSRsyns180NSS[thetaSRcount] = new NetStim(prop)
			InhThetaSRsyns180NCS[thetaSRcount] = new NetCon(InhThetaSRsyns180NSS[thetaSRcount], InhThetaSRsyns180[thetaSRcount])
			InhThetaSRsyns180NCS[thetaSRcount].weight = 0.00000469125*distance(prop) + 0.0002695779

			InhThetaSRsyns270[thetaSRcount] = new Exp2Syn(prop)
			dend[dendn] InhThetaSRsyns270[thetaSRcount].loc(prop)
			InhThetaSRsyns270[thetaSRcount].tau1 = 0.1013
			InhThetaSRsyns270[thetaSRcount].tau2 = 4.8216
			InhThetaSRsyns270[thetaSRcount].e = -70
			InhThetaSRsyns270NSS[thetaSRcount] = new NetStim(prop)
			InhThetaSRsyns270NCS[thetaSRcount] = new NetCon(InhThetaSRsyns270NSS[thetaSRcount], InhThetaSRsyns270[thetaSRcount])
			InhThetaSRsyns270NCS[thetaSRcount].weight = 0.00000469125*distance(prop) + 0.0002695779

			thetaSRcount = thetaSRcount + 1
		}		
		
		// Assign optimized synapse parameter values to 9 excitatory synapses on the compartment if in SLM
		if (distance(prop)>300) { // i.e. if greater than 300 um away from soma
			for (l = 1; l<=9; l = l + 1){
				SLMexcsynapses[SLMexcsyncount] = new Exp2Syn(prop)
				dend[dendn] SLMexcsynapses[SLMexcsyncount].loc(prop) // assign to current compartment
				SLMexcsynapses[SLMexcsyncount].tau1 = 6.1871e-04
				SLMexcsynapses[SLMexcsyncount].tau2 = 3.1975
				SLMexcsynapses[SLMexcsyncount].e = 0
				SLMexcnss[SLMexcsyncount] = new VecStim(prop)
				SLMexcncs[SLMexcsyncount] = new NetCon(SLMexcnss[SLMexcsyncount], SLMexcsynapses[SLMexcsyncount])
				SLMexcncs[SLMexcsyncount].weight = 0.00000230814*distance(prop) + 0.00022016666
				SLMexcsyncount = SLMexcsyncount + 1
			}
			// THETA SYNAPSES
			ExcThetaSLMsyns[thetaSLMcount] = new Exp2Syn(prop)
			dend[dendn] ExcThetaSLMsyns[thetaSLMcount].loc(prop)
			ExcThetaSLMsyns[thetaSLMcount].tau1 = 6.1871e-04
			ExcThetaSLMsyns[thetaSLMcount].tau2 = 3.1975
			ExcThetaSLMsyns[thetaSLMcount].e = 0
			ExcThetaSLMsynsNSS[thetaSLMcount] = new NetStim(prop)
			ExcThetaSLMsynsNCS[thetaSLMcount] = new NetCon(ExcThetaSLMsynsNSS[thetaSLMcount], ExcThetaSLMsyns[thetaSLMcount])
			ExcThetaSLMsynsNCS[thetaSLMcount].weight = 0.00000230814*distance(prop) + 0.00022016666
					
			InhThetaSLMsyns90[thetaSLMcount] = new Exp2Syn(prop)
			dend[dendn] InhThetaSLMsyns90[thetaSLMcount].loc(prop)
			InhThetaSLMsyns90[thetaSLMcount].tau1 = 0.1013
			InhThetaSLMsyns90[thetaSLMcount].tau2 = 4.8216
			InhThetaSLMsyns90[thetaSLMcount].e = -70
			InhThetaSLMsyns90NSS[thetaSLMcount] = new NetStim(prop)
			InhThetaSLMsyns90NCS[thetaSLMcount] = new NetCon(InhThetaSLMsyns90NSS[thetaSLMcount], InhThetaSLMsyns90[thetaSLMcount])
			InhThetaSLMsyns90NCS[thetaSLMcount].weight = 0.00000469125*distance(prop) + 0.0002695779
		
			InhThetaSLMsyns180[thetaSLMcount] = new Exp2Syn(prop)
			dend[dendn] InhThetaSLMsyns180[thetaSLMcount].loc(prop)
			InhThetaSLMsyns180[thetaSLMcount].tau1 = 0.1013
			InhThetaSLMsyns180[thetaSLMcount].tau2 = 4.8216
			InhThetaSLMsyns180[thetaSLMcount].e = -70
			InhThetaSLMsyns180NSS[thetaSLMcount] = new NetStim(prop)
			InhThetaSLMsyns180NCS[thetaSLMcount] = new NetCon(InhThetaSLMsyns180NSS[thetaSLMcount], InhThetaSLMsyns180[thetaSLMcount])
			InhThetaSLMsyns180NCS[thetaSLMcount].weight = 0.00000469125*distance(prop) + 0.0002695779

			InhThetaSLMsyns270[thetaSLMcount] = new Exp2Syn(prop)
			dend[dendn] InhThetaSLMsyns270[thetaSLMcount].loc(prop)
			InhThetaSLMsyns270[thetaSLMcount].tau1 = 0.1013
			InhThetaSLMsyns270[thetaSLMcount].tau2 = 4.8216
			InhThetaSLMsyns270[thetaSLMcount].e = -70
			InhThetaSLMsyns270NSS[thetaSLMcount] = new NetStim(prop)
			InhThetaSLMsyns270NCS[thetaSLMcount] = new NetCon(InhThetaSLMsyns270NSS[thetaSLMcount], InhThetaSLMsyns270[thetaSLMcount])
			InhThetaSLMsyns270NCS[thetaSLMcount].weight = 0.00000469125*distance(prop) + 0.0002695779
			
			thetaSLMcount = thetaSLMcount + 1
		}
		
		// Assign optimized synapse parameter values to 2 inhibitory synapses on the compartment
		for (m = 1; m<=2; m = m + 1){
			inhsynapses[inhsyncount] = new Exp2Syn(prop)
			dend[dendn] inhsynapses[inhsyncount].loc(prop) // assign to current compartment
			inhsynapses[inhsyncount].tau1 = 0.1013
			inhsynapses[inhsyncount].tau2 = 4.8216
			inhsynapses[inhsyncount].e = -70
			inhnss[inhsyncount] = new VecStim(prop)
			inhncs[inhsyncount] = new NetCon(inhnss[inhsyncount], inhsynapses[inhsyncount])
			inhncs[inhsyncount].weight = 0.00000469125*distance(prop) + 0.0002695779
			inhsyncount = inhsyncount + 1
		}
		count = count + 1		
	}	
}

// Generate randomized indexing for random synapse selection
objref r, randSRexcindex, randSLMexcindex, randinhindex, EXCrandSRtheta, EXCrandSLMtheta
objref randSRinhtheta90, randSRinhtheta180, randSRinhtheta270, randSLMinhtheta90, randSLMinhtheta180, randSLMinhtheta270

proc randomize_syns() {
	r = new Random($1*10 + $2) // Ensures different random seeds for each example and example repeat
	randSRexcindex = new Vector(nSRexcsyns)
	randSLMexcindex = new Vector(nSLMexcsyns)

	EXCrandSRtheta = new Vector(thetaSRcount)
	randSRinhtheta90 = new Vector(thetaSRcount)
	randSRinhtheta180 = new Vector(thetaSRcount)
	randSRinhtheta270 = new Vector(thetaSRcount)
		
	EXCrandSLMtheta = new Vector(thetaSLMcount)
	randSLMinhtheta90 = new Vector(thetaSRcount)
	randSLMinhtheta180 = new Vector(thetaSRcount)
	randSLMinhtheta270 = new Vector(thetaSRcount)
	
	randinhindex = new Vector(ninhsyns)

	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < nSRexcsyns; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, nSRexcsyns-1) // Generate random integer
			for k=0,nSRexcsyns-1 repeats = repeats + (tempindex == randSRexcindex.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSRexcindex.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < nSLMexcsyns; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, nSLMexcsyns-1) // Generate random integer
			for k=0,nSLMexcsyns-1 repeats = repeats + (tempindex == randSLMexcindex.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSLMexcindex.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < ninhsyns; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, ninhsyns-1) // Generate random integer
			for k=0,ninhsyns-1 repeats = repeats + (tempindex == randinhindex.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randinhindex.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}

	// Theta Randomizations
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSRcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSRcount-1) // Generate random integer
			for k=0,thetaSRcount-1 repeats = repeats + (tempindex == EXCrandSRtheta.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		EXCrandSRtheta.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSRcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSRcount-1) // Generate random integer
			for k=0,thetaSRcount-1 repeats = repeats + (tempindex == randSRinhtheta90.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSRinhtheta90.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSRcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSRcount-1) // Generate random integer
			for k=0,thetaSRcount-1 repeats = repeats + (tempindex == randSRinhtheta180.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSRinhtheta180.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSRcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSRcount-1) // Generate random integer
			for k=0,thetaSRcount-1 repeats = repeats + (tempindex == randSRinhtheta270.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSRinhtheta270.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSLMcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSLMcount-1) // Generate random integer
			for k=0,thetaSLMcount-1 repeats = repeats + (tempindex == EXCrandSLMtheta.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		EXCrandSLMtheta.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSLMcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSLMcount-1) // Generate random integer
			for k=0,thetaSLMcount-1 repeats = repeats + (tempindex == randSLMinhtheta90.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSLMinhtheta90.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSLMcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSLMcount-1) // Generate random integer
			for k=0,thetaSLMcount-1 repeats = repeats + (tempindex == randSLMinhtheta180.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSLMinhtheta180.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
	tempindex = 0
	repeats = 1 // Initialize at 1 so it does skip the while loop
	for (i = 0; i < thetaSLMcount; i = i + 1){
		while (repeats > 0){
			repeats = 0 // Reset the count of repeats to 0 for next iteration
			tempindex = r.discunif(-1, thetaSLMcount-1) // Generate random integer
			for k=0,thetaSLMcount-1 repeats = repeats + (tempindex == randSLMinhtheta270.x[k]) // Check if value repeats (i.e. if repeats > 0)
		}
		randSLMinhtheta270.x[i] = tempindex // Assign value if not repeated
		repeats = 1 // Re-initialize to 1 so it doesn't skip while loop
	}
}

access soma
// Create new synapses to generate theta-timed spiking
objectvar sw, apc, apctimes, rSRexc, rSRexcvec, rSLMexc, rSLMexcvec, rinh, rinhvec, frecSRExcPreSpikeTrains, frecSLMExcPreSpikeTrains, frecInhPreSpikeTrains, rSRexcMat, rSLMexcMat, rinhMat

access soma
distance()

// Record presynaptic theta spike times
objectvar ThetaSRexcprespiketrains[500], ThetaSLMexcprespiketrains[500], thetaMat, frecThetaSpikeTrains
objectvar ThetaSRInh90prespiketrains[500], ThetaSRInh180prespiketrains[500], ThetaSRInh270prespiketrains[500], ThetaSLMInh90prespiketrains[500], ThetaSLMInh180prespiketrains[500], ThetaSLMInh270prespiketrains[500]

objref spTheta, spHC
spTheta = new Shape()
spTheta.show(0)
spHC = new Shape()
spHC.show(0)
thetamultiplier = 0

proc f() {
	spHC = new Shape()
	spHC.show(0)
	
	rSRexc = new Random($6*10+$7+28293) // Ensures different random seeds on each iteration
	rSRexc.uniform(0,tstop)
	rSLMexc = new Random($6*10+$7+51234)
	rSLMexc.uniform(0,tstop)
	rinh = new Random($6*10+$7+81221)
	rinh.uniform(0,tstop)
	
	inhsyncount = $1
	excsyncount = $2
	inhsynspikes = $3
	excSRsynspikes = $4
	excSLMsynspikes = $4
	SaveExample = $5
	nexccommon = 9
	ninhcommon = 4
	AddRhythm = $8
	
	inhthetacount = $9
	excthetacount = $10
	EXCSLM = $11
	EXCSR = $12
	
	Inh90SR = $13
	Inh180SR = $14
	Inh270SR = $15
	Inh90SLM = $16
	Inh180SLM = $17
	Inh270SLM = $18
	
	// Re-initialize all inhibitory synapses such that they are silent when starting a new iteration
	rinhvec = new Vector(0)
	for i=0,ninhsyns-1 inhnss[randinhindex.x[i]].play(rinhvec)
	
	// Re-initialize all excitatory synapses such that they are silent when starting a new iteration
	rSRexcvec = new Vector(0)
	for i=0,nSRexcsyns-1 SRexcnss[randSRexcindex.x[i]].play(rSRexcvec)
	rSLMexcvec = new Vector(0)
	for i=0,nSLMexcsyns-1 SLMexcnss[randSLMexcindex.x[i]].play(rSLMexcvec)
	
	// Assign excitatory spike times
	if (excSRsynspikes > 0 && excSLMsynspikes > 0) {
		rSRexcMat = new Matrix(int((excsyncount)/2),excSRsynspikes)
		rSLMexcMat = new Matrix(int((excsyncount)/2),excSLMsynspikes)
		for (i=0; i < int((excsyncount)/2); i = i + 1){ // On each iteration add 1 SR and 1 SLM excitatory synapse
	
			// Sample new spike times for common inputs
			rSRexcvec = new Vector(excSRsynspikes)
			rSRexcvec.setrand(rSRexc)
			rSRexcvec.sort()
			rSLMexcvec = new Vector(excSLMsynspikes)
			rSLMexcvec.setrand(rSLMexc)
			rSLMexcvec.sort()

			xcom = 1
			// Common input loop where synapses are given the same input until the maximum number of common inputs is passed
			while (xcom <= nexccommon && i < int((excsyncount)/2) && i < nSLMexcsyns && i < nSRexcsyns) {
		
				// Add SR excitatory inputs
				SRexcnss[randSRexcindex.x[i]].play(rSRexcvec)
				spHC.point_mark(SRexcsynapses[randSRexcindex.x[i]],3,"O",2)
				for k=0,excSRsynspikes-1 rSRexcMat.x[i][k] = rSRexcvec.x[k]
		
				// Add SLM excitatory inputs and if out of SLM synapses add SR inputs intead
				SLMexcnss[randSLMexcindex.x[i]].play(rSLMexcvec)
				spHC.point_mark(SLMexcsynapses[randSLMexcindex.x[i]],4,"O",2)
				for k=0,excSLMsynspikes-1 rSLMexcMat.x[i][k] = rSLMexcvec.x[k]
	
				i = i + 1 // update indexing
				xcom = xcom + 1
			}
			i = i - 1 // i.e. so that i does not get updated twice resulting in skipped synapses
		}
	}

	// Assign inhibitory spike times
	if (inhsynspikes > 0){
		rinhMat = new Matrix(inhsyncount,inhsynspikes)
		for (i=0; i < inhsyncount; i = i + 1){
			rinhvec = new Vector(inhsynspikes)
			rinhvec.setrand(rinh) 
			rinhvec.sort()

			xcom = 1
			while (xcom <= ninhcommon && i < inhsyncount) {
				inhnss[randinhindex.x[i]].play(rinhvec)
				spHC.point_mark(inhsynapses[randinhindex.x[i]],2,"O",1.5)
				// Build Spike Time Matrix
				for k=0,inhsynspikes-1 rinhMat.x[i][k] = rinhvec.x[k]

				i = i + 1
				xcom = xcom + 1
			}
			i = i - 1 // i.e. so that i does not get updated twice resulting in skipped synapses
		}
	}
	
	// Re-Initialize All Theta Inputs
	for (p = 0; p < thetaSLMcount; p = p + 1){
		ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].interval = tstop
		ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].number = 0
		ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].start = tstop
		ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].noise = 0
		
		InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].interval = tstop
		InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].number = 0
		InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].start = tstop
		InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].noise = 0
		
		InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].interval = tstop
		InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].number = 0
		InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].start = tstop
		InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].noise = 0
		
		InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].interval = tstop
		InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].number = 0
		InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].start = tstop
		InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].noise = 0
	}
	for (p = 0; p < thetaSRcount; p = p + 1){
		ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].interval = tstop
		ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].number = 0
		ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].start = tstop
		ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].noise = 0
		
		InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].interval = tstop
		InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].number = 0
		InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].start = tstop
		InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].noise = 0
	
		InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].interval = tstop
		InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].number = 0
		InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].start = tstop
		InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].noise = 0
	
		InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].interval = tstop
		InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].number = 0
		InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].start = tstop
		InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].noise = 0
	}
	
	// Feed theta inputs to desired areas
	for (p = 0; p < excthetacount*AddRhythm; p = p + 1){
		if (EXCSLM == 1){
			ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].start = 0
			ExcThetaSLMsynsNSS[EXCrandSLMtheta.x[p]].noise = 0
			spTheta.point_mark(ExcThetaSLMsyns[EXCrandSLMtheta.x[p]],4,"O",2)
			ThetaSLMexcprespiketrains[EXCrandSLMtheta.x[p]] = new Vector()
			ExcThetaSLMsynsNCS[EXCrandSLMtheta.x[p]].record(ThetaSLMexcprespiketrains[EXCrandSLMtheta.x[p]])
		}
		if (EXCSR == 1){
			ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].start = 31.25
			ExcThetaSRsynsNSS[EXCrandSRtheta.x[p]].noise = 0
			spTheta.point_mark(ExcThetaSRsyns[EXCrandSRtheta.x[p]],3,"O",2)
			ThetaSRexcprespiketrains[EXCrandSRtheta.x[p]] = new Vector()
			ExcThetaSRsynsNCS[EXCrandSRtheta.x[p]].record(ThetaSRexcprespiketrains[EXCrandSRtheta.x[p]])
		}
	}
	
	for (p = 0; p < inhthetacount*AddRhythm; p = p + 1){
		if (Inh90SR == 1){
			InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].start = 31.25
			InhThetaSRsyns90NSS[randSRinhtheta90.x[p]].noise = 0
			spTheta.point_mark(InhThetaSRsyns90[randSRinhtheta90.x[p]],2,"O",1.5)
			ThetaSRInh90prespiketrains[randSRinhtheta90.x[p]] = new Vector()
			InhThetaSRsyns90NCS[randSRinhtheta90.x[p]].record(ThetaSRInh90prespiketrains[randSRinhtheta90.x[p]])
		}
		if (Inh180SR == 1){
			InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].start = 62.5
			InhThetaSRsyns180NSS[randSRinhtheta180.x[p]].noise = 0
			spTheta.point_mark(InhThetaSRsyns180[randSRinhtheta180.x[p]],5,"O",1.5)
			ThetaSRInh180prespiketrains[randSRinhtheta180.x[p]] = new Vector()
			InhThetaSRsyns180NCS[randSRinhtheta180.x[p]].record(ThetaSRInh180prespiketrains[randSRinhtheta180.x[p]])
		}
		if (Inh270SR == 1){
			InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].start = 93.75
			InhThetaSRsyns270NSS[randSRinhtheta270.x[p]].noise = 0
			spTheta.point_mark(InhThetaSRsyns270[randSRinhtheta270.x[p]],6,"O",1.5)
			ThetaSRInh270prespiketrains[randSRinhtheta270.x[p]] = new Vector()
			InhThetaSRsyns270NCS[randSRinhtheta270.x[p]].record(ThetaSRInh270prespiketrains[randSRinhtheta270.x[p]])
		}
		if (Inh90SLM == 1){
			InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].start = 31.25
			InhThetaSLMsyns90NSS[randSLMinhtheta90.x[p]].noise = 0
			spTheta.point_mark(InhThetaSLMsyns90[randSLMinhtheta90.x[p]],2,"O",1.5)
			ThetaSLMInh90prespiketrains[randSLMinhtheta90.x[p]] = new Vector()
			InhThetaSLMsyns90NCS[randSLMinhtheta90.x[p]].record(ThetaSLMInh90prespiketrains[randSLMinhtheta90.x[p]])
		}
		if (Inh180SLM == 1){
			InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].start = 62.5
			InhThetaSLMsyns180NSS[randSLMinhtheta180.x[p]].noise = 0
			spTheta.point_mark(InhThetaSLMsyns180[randSLMinhtheta180.x[p]],5,"O",1.5)
			ThetaSLMInh180prespiketrains[randSLMinhtheta180.x[p]] = new Vector()
			InhThetaSLMsyns180NCS[randSLMinhtheta180.x[p]].record(ThetaSLMInh180prespiketrains[randSLMinhtheta180.x[p]])
		}
		if (Inh270SLM == 1){
			InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].interval = (1/8)*1000 // i.e. 8Hz converted to a time interval in ms
			InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].number = 8*tstop/1000 // i.e. if 8 Hz, there should be 80 presynaptic spikes in 10 seconds (per synapse)
			InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].start = 93.75
			InhThetaSLMsyns270NSS[randSLMinhtheta270.x[p]].noise = 0
			spTheta.point_mark(InhThetaSLMsyns270[randSLMinhtheta270.x[p]],6,"O",1.5)
			ThetaSLMInh270prespiketrains[randSLMinhtheta270.x[p]] = new Vector()
			InhThetaSLMsyns270NCS[randSLMinhtheta270.x[p]].record(ThetaSLMInh270prespiketrains[randSLMinhtheta270.x[p]])
		}
	}
	
	if (SaveExample==1){
		if (AddRhythm == 1){ // Change later when adding more synapses
			// Save Excitatory Raster Matrices
			sprint(filename4,"SRExcPreSpikeTrains_%g_NumInh_%g_NumExc_%g_InhSpikes_%g_ExcSRSpikes_%g_ExcSLMSpikes_%g_NumExcCommon_%g_NumInhCommon_X%g_ThetaMultiplier.dat",inhsyncount,excsyncount,inhsynspikes,excSRsynspikes,excSLMsynspikes,nexccommon,ninhcommon,inhthetacount/8)
			frecSRExcPreSpikeTrains = new File(filename4)
			frecSRExcPreSpikeTrains.wopen(filename4)
			if (excSRsynspikes > 0) {
				rSRexcMat.fprint(frecSRExcPreSpikeTrains,"%f\t") // Spike times sampled from random distribution
			}
			frecSRExcPreSpikeTrains.close()

			sprint(filename7,"SLMExcPreSpikeTrains_%g_NumInh_%g_NumExc_%g_InhSpikes_%g_ExcSRSpikes_%g_ExcSLMSpikes_%g_NumExcCommon_%g_NumInhCommon_X%g_ThetaMultiplier.dat",inhsyncount,excsyncount,inhsynspikes,excSRsynspikes,excSLMsynspikes,nexccommon,ninhcommon,inhthetacount/8)
			frecSLMExcPreSpikeTrains = new File(filename7)
			frecSLMExcPreSpikeTrains.wopen(filename7)
			if (excSLMsynspikes > 0) {
				rSLMexcMat.fprint(frecSLMExcPreSpikeTrains,"%f\t") // Spike times sampled from random distribution
			}
			frecSLMExcPreSpikeTrains.close()

			// Save Inhibitory Raster Matrix
			sprint(filename5,"InhPreSpikeTrains_%g_NumInh_%g_NumExc_%g_InhSpikes_%g_ExcSRSpikes_%g_ExcSLMSpikes_%g_NumExcCommon_%g_NumInhCommon_X%g_ThetaMultiplier.dat",inhsyncount,excsyncount,inhsynspikes,excSRsynspikes,excSLMsynspikes,nexccommon,ninhcommon,inhthetacount/8)
			frecInhPreSpikeTrains = new File(filename5)
			frecInhPreSpikeTrains.wopen(filename5)
			if (inhsynspikes > 0){
				rinhMat.fprint(frecInhPreSpikeTrains,"%f\t") // Spike times sampled from random distribution
			}
			frecInhPreSpikeTrains.close()
			
			sprint(filename3,"HCSynLocationsShapePlot_1_HCNumber.ps")
			spHC.printfile(filename3)
			spHC.point_mark_remove()
			
			sprint(filename6,"ThetaSynLocationsShapePlot_X%g_ThetaMultiplier.ps",thetamultiplier)
			spTheta.printfile(filename6)
			spTheta.point_mark_remove()
			thetamultiplier = thetamultiplier + 1
		}
		apc = new APCount(0.5)
		apctimes = new Vector()
		apc.thresh = -20
		apc.record(apctimes)
		
		// Run Simulation and Record Vm Vector
		recV = new Vector()
		recV.record(&soma.v(0.5))
		run()
		sprint(filename1,"model_%g_NumInh_%g_NumExc_%g_InhSpikes_%g_ExcSRSpikes_%g_ExcSLMSpikes_%g_NumExcCommon_%g_NumInhCommon_X%g_ThetaMultiplier.dat",inhsyncount,excsyncount,inhsynspikes,excSRsynspikes,excSLMsynspikes,nexccommon,ninhcommon,inhthetacount/8)
		frecV = new File(filename1)
		frecV.wopen(filename1)
		recV.vwrite(frecV) // Use printf instead of vwrite if you want a text file instead of a binary file
		frecV.close()
			
		// if (AddRhythm == 1){
		// 	numindices = excthetacount*(EXCSLM+EXCSR) + inhthetacount*(OLMSLM+NGFSLM+IS2SLM+BISSR+IS1SR)
		// 	// Build Theta Spike Matrix
		// 	numSpikes = 8*tstop/1000
		// 	thetaMat = new Matrix(numindices,numSpikes)
		// 	for (x = 0; x < excthetacount*EXCSLM; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSLMexcprespiketrains[EXCrandSLMtheta.x[x]].x[y]
		// 	}
		// 	for (x = excthetacount*EXCSLM; x < excthetacount*EXCSLM + excthetacount*EXCSR; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSRexcprespiketrains[EXCrandSRtheta.x[x-(excthetacount*EXCSLM)]].x[y]
		// 	}
		// 	for (x = excthetacount*EXCSLM + excthetacount*EXCSR; x < excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSLMOLMprespiketrains[OLMrandSLMtheta.x[x-(excthetacount*EXCSLM + excthetacount*EXCSR)]].x[y]
		// 	}
		// 	for (x = excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM; x < excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSLMNGFprespiketrains[NGFrandSLMtheta.x[x-(excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM)]].x[y]
		// 	}
		// 	for (x = excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM; x < excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSLMIS2prespiketrains[IS2randSLMtheta.x[x-(excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM)]].x[y]
		// 	}
		// 	for (x = excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM; x < excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM + inhthetacount*BISSR; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSRBISprespiketrains[BISrandSRtheta.x[x-(excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM)]].x[y]
		// 	}
		// 	for (x = excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM + inhthetacount*BISSR; x < excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM + inhthetacount*BISSR + inhthetacount*IS1SR; x = x + 1){
		// 		for y = 0,numSpikes-1 thetaMat.x[x][y] = ThetaSRIS1prespiketrains[IS1randSRtheta.x[x-(excthetacount*EXCSLM + excthetacount*EXCSR + inhthetacount*OLMSLM + inhthetacount*NGFSLM + inhthetacount*IS2SLM + inhthetacount*BISSR)]].x[y]
		// 	}
		// 	//Save Theta Spike Matrix
		// 	sprint(filename2,"ThetaSpikeTrains_%g_NumInh_%g_NumExc_%g_InhSpikes_%g_ExcSRSpikes_%g_ExcSLMSpikes_%g_NumExcCommon_%g_NumInhCommon_%g_ThetaMultiplier.dat",inhsyncount,excsyncount,inhsynspikes,excSRsynspikes,excSLMsynspikes,nexccommon,ninhcommon,inhthetacount/8)
		// 	frecThetaSpikeTrains = new File(filename2)
		// 	frecThetaSpikeTrains.wopen(filename2)
		// 	thetaMat.fprint(frecThetaSpikeTrains,"%f\t") // Spike times sampled from random distribution
		// 	frecThetaSpikeTrains.close()
		// }
	}else{
		// Run Simulation and Record Vm Vector
		recV = new Vector()
		recV.record(&soma.v(0.5))
		run()
	}
}

Loading data, please wait...