Normal ripples, abnormal ripples, and fast ripples in a hippocampal model (Fink et al. 2015)

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"...We use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples."
1 . Fink CG, Gliske S, Catoni N, Stacey WC (2015) Network Mechanisms Generating Abnormal and Normal Hippocampal High-Frequency Oscillations: A Computational Analysis. eNeuro [PubMed]
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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 CA1 basket cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; NMDA; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Epilepsy;
Implementer(s): Fink, Christian G. [cgfink at]; Gliske, Stephen [sgliske at]; Stacey, William [wstacey at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; NMDA; Glutamate; I Na,t; I A; I K; I h;
{load_file("nrngui.hoc") }         //I assume these libraries will load from the first /nrn path

	//print "doing EXTRACELLULAR stuff..........."
	{load_file("./interpxyz.hoc")}	// only interpolates sections that have extracellular
	{load_file("./setpointers.hoc")}  // has the function grindaway() in interpxyz.hoc to set up pointers
	{load_file("./field.hoc")} //functions to calculate the extracellular voltage
	{load_file("./calcrxc_a.hoc")}  //function to create the transfer resistance from each section

strdef cmd
objref pr, pc, nil, pyrspiketau_vec, baskspiketau_vec, baskconnvector, ipspg_vec, fih_progress, hines


gapstyle=96  //split baskets right, many tests

//19 lowered spikedur to 75
//20 from here onward the _b will actually be basketnoise, rather than conn %.  I've switched conn to 100
//g97 is to signify switching to this new method
gapstyle=0  //this is to signify running it with the newer version, with the noisesyn mods added

//29 ungapped baskets, 2 rows of 10 inline gaps between pyr cells (.01 - high) 1-10 and 41-50
antennaDC=1.3 //constant current added to antenna cells

//celsius = 34   //for some reason this line gives an error for being out of range

//the following variables determine the structure of the ramp in noise
/* ramp_on=100
ramp_num_steps=100 */

num_trials=1  //set number of different trials for each combination of pyrspike_tau, baskspike_tau, baskconnvector, and ipspgmax. There will be a different global seed for each simulation
gind_start=0  //global index at which to start, in the parameter sweep
{pyrspiketau_vec = new Vector()}
{baskspiketau_vec = new Vector()}
{baskconnvector = new Vector()}
{ipspg_vec = new Vector()}
{pyrspiketau_vec.append(0.10)} //mean time between arrival of noise events to pyramidal cells (so smaller number implies more intense noise)
{baskspiketau_vec.append(6.0)}  // 1e9 orig.: mean time between arrival of noise events to basket cells (so smaller number implies more intense noise)
{baskconnvector.append(0)}    //controls what percentage of possible connections from basket cells to pyramidal cells are realized
{ipspg_vec.append(5.5)}  //gmax for bask->pyr synapses, which goes into _f# in the filename
//29 was old method
//30 is adding in chris' new pieces one by one.  First is having shortspikes and noisesyn.  It worked
//31 is having baskets 80-99 and ant above.  Working
//32 recording from ant cells better (was same recording until now).  Working?
//33 gapped baskets to see ant cell response-  gaps worked, but no real response. 
//34  Will add bias current to ants, chose 1.5 to make it AP, not enough, made it 3 and now it works.
//35 adding noisesyn to baskets.  worked
//36 adding noisesyn to ant, and activateAntSynapses.. worked.  Makes normal HFO (high basket gaps, ant/bask/pyr all same noise (.33/ 3.333).  Also two chains of pyr gaps
//37 trying to make abnormal HFO.  (.2 to all no real change, .1 and basketstart 1e9 looks good)
//38 same as above without antenna's active (just antenna for baskets, not for spike).  manymoreconn has one additional gap between 88 and 90 to remove the
//   "split".  600 ant cells.  baskets and pyr both active. 
//39  3000 antenna, had to change the location style

pc = new ParallelContext()

func getTstop() { return Tstop }

proc prinit() {    //had to change name because non-pr functions couldn't address pr

	{pr.setScatteredVoltages(-85, -60)}

	{pr.connectNetwork($1,$2)}  // took this out of init() in TGbignet2.tem
	{pr.setSeed($3)} //set global index for Random123 
	// { pr.activeSynapsesZero()} //CF: This inactivates all connections. I have no idea why you would want to do this.

func onerun() {local id, num, ipspg, pyrthr, basketthr, pyr_spike_tau, bask_spike_tau, bask_perc, temp_time, temp_tau,ii localobj pc, fo, fo1, forast
	id= hoc_ac_
	pc = new ParallelContext()
	{pr  = new TGbignet2()}
	if(doextra) {
		print "Doing EXTRACELLULAR stuff.....    ....."
		setelec(-50, 0, 0)
	//hines = new FInitializeHandler(2, "hinest1=startsw() hinest2=startsw() hines1()")
	print "id_world number ", pc.id_world, "  id_bbs ", pc.id_bbs, "  id   ", id, "  ", 
	//fih_progress = new FInitializeHandler(2, "cvode.event(100, \"progress()\" )" )  //took out if ( == 0) 
	//forall { for (x,0) print secname() }
	runningTime = startsw()

	bask_perc=$3  //percentage of inhibitory connections that are allowed to exist
	prinit(bask_perc,ipspg,g_ind)   //added this extra function to allow for non-pr functions.  The input is passed to connectNetwork as the connthr
	normmean=0    //can set this to $5 if useful in the future

	pyr_nospike_tau=1.0 // 1e9
	bask_nospike_tau=6.0 // 1e9
	{ pr.activatePyrSynapses(pyr_spike_tau,pyr_nospike_tau) }
	{ pr.activateAntSynapses(pyr_spike_tau,pyr_nospike_tau) }
	{ pr.activateBaskSynapses(bask_spike_tau,bask_nospike_tau) }

	//we no longer need shortspikes, because noisesyn.mod provides stochastic stimulation; do need to pilfer 'addAntennaDC' from shortspikes, though
	// if (shortspikeFlag) {
	//	{pr.shortspikes(Tstop,pyrthr,basketthr, spikedur, spikefreq, normmean, normvar)} 
	/* } else {
		//{pr.shortnonrandomspikes(Tstop,pyrthr,basketthr, spikedur, spikefreq)} } else {
		//{pr.singlecellnonrandom(Tstop,pyrthr,basketthr, spikedur, spikefreq)} } else {
		{pr.activeSynapsesRandom(Tstop, pyrthr, basketthr)}  
	} */
	{ pr.addAntennaDC(antennaDC) } //CF: add DC current to all antenna cells, so that their resting membrane potential is above the -80mV chloride reversal potential; spike threshold is slightly above 1.3, pyrthr, ipspg, basketthr)	
	forast = new File()
	sprint(cmd, "data/spikes_b%4.2f_p%4.2f_g%4.2f_f%d.dat", bask_spike_tau, pyr_spike_tau, ipspg,bask_perc)
	{ forast.wopen(cmd) }
	//now set up all the cvode events which will change the pyr_spike_tau values delivered to the pyramidal cells
	/* for ii=1, ramp_num_steps {
		temp_time=ramp_on + ii*(ramp_off-ramp_on)/ramp_num_steps  //calculate the time at which the iith noise change should occur
		temp_tau=ramp_tau_start + ii*(ramp_tau_end-ramp_tau_start)/ramp_num_steps //calculate the value of tau for the iith  noise change
		sprint(cmd,"pr.activatePyrSynapses(%5.3f,pyr_nospike_tau,normalstd)",temp_tau) //create the command to use 'activatePyrSynapses' to change the value of pyr_spike_tau
	} */
	//advance through simulation in increments of t_seg (defined in externals.hoc); after every t_seg, write voltage data to files, and delete vectors containing this data,
	//so that program does not run out of memory
	t_curr = 0
	while (t_curr < Tstop-dt){ //include the '-dt' to account for rounding error; otherwise, may get error in writeVoltages
		print "Time = ",t_curr
		if(t_curr + t_seg < Tstop) {
			{ pr.pnm.pcontinue(t_curr+t_seg)}
		} else {
		for i=0, pr.pnm.spikevec.size-1 {
			forast.printf("%-10.6lf, %d\n", pr.pnm.spikevec.x[i], pr.pnm.idvec.x[i])
		pr.writeVoltages(bask_spike_tau, pyr_spike_tau, ipspg, bask_perc,t_curr)
		t_curr = t_curr + t_seg
	//basketthr=$2 //reset in case I used it above differently

	//if (realrunFlag) {pr.writeVoltages(basketthr, pyrthr, gapstyle, sigfreq)} 
	runningTime = startsw() - runningTime
	print "Running Time: ", runningTime, "iteration: ",iteration
	/* {pr.pnm.gatherspikes()}
  	fo = new File()
	fo1= new File()
	sprint(cmd, "data/spikes.dat")
	sprint(cmd, "data/spikes_b%d_p%d_g%d_f%d.dat", basketthr, pyrthr, gapstyle,sigfreq)
	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()}   //remove this one?

 	if (realrunFlag){
		{fo=new File()}
		{sprint(cmd, "spikes_b%4.2f_p%5.3f_g%4.2f_f%d.dat", bask_spike_tau, pyr_spike_tau,ipspg,bask_perc)}
		{fo=new File()}
		{sprint(cmd, "sum_b%4.2f_p%5.3f_g%4.2f_f%d.dat", bask_spike_tau, pyr_spike_tau,ipspg,bask_perc)}
		pr.writeParameters(bask_spike_tau, pyr_spike_tau, gapstyle, ipspg, bask_nospike_tau, pyr_nospike_tau, antennaDC, Tstop, t_seg) //write parameters to file

	return id

proc progress() {
	print t
	cvode.event(t+100, "progress()" )

proc hines1() {
        printf("pc.id_world= %d pc.id_bbs= %d t= %g dt= %g dreal=%g treal=%g\n", pc.id_world, pc.id_bbs, t, dt, startsw()-hinest2, startsw()-hinest1)
        hinest2 = startsw()
        cvode.event(t + 10, "hines1()")


proc series() {local i, j, k, delay, tstop, id, spkcnt, tmax, gid, num

	for i = 0, pyrspiketau_vec.size()-1 {
		for j = 0, baskspiketau_vec.size()-1 {
			for k = 0, baskconnvector.size()-1 {
			  for l = 0, ipspg_vec.size()-1 {
			  		for m = 0, num_trials-1 {
						//generate a different global index for each simulation
						g_index = gind_start+i*baskspiketau_vec.size()*baskconnvector.size()*ipspg_vec.size()*num_trials + j*baskconnvector.size()*ipspg_vec.size()*num_trials + k*ipspg_vec.size()*num_trials + l*num_trials + m
						{pc.submit("onerun", pyrspiketau_vec.x[i], baskspiketau_vec.x[j], baskconnvector.x[k], ipspg_vec.x[l],g_index)}
	while ((id= pc.working())!=0) {
		pc.take(id, &num)
		//printf("num= %d", num)

series() //this line actually runs the simulation

//onerun(45,90,1, 400)  //pyrthr, baskthr, f, normvar