// This experiment is used to study the effect of synapse clustering
// on the firing rate of our model cell. To this end, we perform a gradual
// dispersion of synaptic 45, 48, 49 or 63 contacts onto the thin oblique dendrites.
// We start with the optimal number of clusters each consisting of the optimal
// number of synapses and in each step we destroy one cluster and disperse
// it's synapses until we end up with no intact clusters.
// Type of inhibition included: 11 gabaa/gabab synapses, 5 in soma sections and 6
// every 60um along the apical trunk, plus 6 gabaa/gabab synapses in tuft sections (> 300um)
// Tuft inhibion in included for large numbers of synaptic stimulation because the tuft
// is very excitable
// Cluster_size x Cluster_number cases tested with this file: 5x9, 9x5, 6x8, 8x6, 4x12, 7x7, 7x9, 9x7
// Synchronous (synch=1) or asynchronous (synch=0) cases can also be tested
// In this experiment we can also include background synapses (50 excitatory and 50 inhibitory)
// firing in 5Hz for the entire duration of the experiments to account for background activity
BACK_GROUND = 0 // set to 1 when using background synapses
synch = 0 // synapses are stimulated randomly (NOT synchronously), set to 1 for synchronous stimulation
//load_proc("nrnmainmenu")
//load_template("ExperimentControl") // load needed templates
//load_template("EPSPTuning")
//load_template("RangeRef")
load_file("nrngui.hoc")
load_file("../../template/load_templates.hoc")
objref econ // initialize template parameters
show_errs=1
debug_lev=1
econ=new ExperimentControl(show_errs,debug_lev)
econ.self_define(econ)
econ.morphology_dir = "../../morphology/n123" // set location for morphology files
econ.add_lib_dir("Terrence","../../lib") // set location for library files
econ.generic_dir = "../../experiment/" // set location for cell-setup file
econ.data_dir = "data" // set directory to store data
strdef tips_dir
tips_dir = "../single-branch-potency/data/Apical_Tips" // set directory with single branch
sprint(econ.syscmd, "mkdir -p %s", econ.data_dir) // 50 Hz stimulation results
system(econ.syscmd)
actual_resolution=75 // maximum nseg number
desired_resolution=1
econ.xopen_geometry_dependent("cell") // load raw cell morphology
econ.xopen_geometry_dependent("cell-analysis") // load user-defined semantics on morphology
cell_analysis(econ)
printf("Opening cell setup\n")
econ.xopen_generic("cell-setup") // load cell-setup to
printf("Opened. Setting up cell\n") // specify all mechanisms,
maximum_segment_length=actual_resolution // membrane properties etc
cell_setup(econ)
// Set simulation parameters for the experiment
econ.defvar("Simulation Control", "tstop", "600", "Defines when the simulation stops.")
econ.defvar("Simulation Control", "dt", "0.1", "Timestep")
econ.defvar("Simulation Control", "steps_per_ms", "10", "How many points are plotted per ms")
setdt()
// open files with NMDA/AMPA, GABA_A/AMPA and GABA_B/GABA_A ratios
econ.xopen_geometry_dependent("nmda-ampa-ratio")
econ.xopen_geometry_dependent("gabab-gabaa-uniform-ratio") // use to estimate GABA_A, GABA_B conductances
// Open file with tuned AMPA conductance values for all sections
objref tune_epsp_list
tune_epsp_list=new List()
strdef tunings_file
sprint(tunings_file, "%s", "tunings")
xopen("../tune-synapses/tunings.dat")
// Open library functions that will be needed
econ.xopen_library("Terrence","choose-secs") // used to randomly select sections from a list
econ.xopen_library("Terrence","salloc") // used to allocate synapses on sections
econ.xopen_library("Terrence","deduce-ratio") // used to extract NMDA/AMPA, GABA_A/AMPA and GABA_B/GABA_B ratios
econ.xopen_library("Terrence","basic-graphics") // used to plot graphics
econ.xopen_library("Terrence","spikecount") // used to count spikes
load_template("SynapseBand") // template for making bands of synapses
// use 5x9, 9x5, 8x6, 6x8, 7x7, 7x9, 9x7
// for cluster_size x cluster_number = 8x6 case
// init_cluster_number = 6 // number of full-sized clusters of synapses
// init_cluster_size = 8 // number of synapses per cluster
// disp_size = 1 // dispersion step size, one cluster at a time
// Cut the cluster size in two, 12x4
// for cluster_size x cluster_number = 4x12 case
// init_cluster_number = 12 // number of full-sized clusters of synapses
// init_cluster_size = 4 // number of synapses per cluster
// disp_size = 2 // dispersion step size, two clusters at a time
gmax_default = 0.0005 // GABA_A explicit conductance value (S/cm^2)
gmaxb_default = gmax_default/3 // GABA_B explicit conductance value (S/cm^2)
all_synapses=init_cluster_size*init_cluster_number // total number of AMPA/NMDA synapses used
gaba_synapses = 100 // maximum number of GABA synapses used
// used with background synapses
back_syne = 50
back_syni = 50
objref input, co
input = new Vector()
co = new Vector()
experiments = init_cluster_number/disp_size + 1 // number of experiments to run
print "Number of experiments ", experiments
double exp_ta[experiments]
for i=0, experiments-1 {
exp_ta[i]= init_cluster_number - i*disp_size // number of intact clusters
printf("-----------------------------------\n")
printf("Experiment %d\n", i)
printf("Number of Intact Clusters: %d\n", exp_ta[i] )
}
// define variables
objref vf, vf2, tmpo, vrec, spikevec, band, vrec, splot, rpid, i_nmda[40], g
objref ampa[all_synapses], nmda[all_synapses], gabaa[gaba_synapses], gabab[gaba_synapses]
objref ampa_bg[back_syne], nmda_bg[back_syne], gabaa_bg[back_syni], gabab_bg[back_syni]
objref apical_tipl, cluster_list, random_list, inh_list, taft_list
strdef tmpstr, tmpstr2, Fc
Deadtime_GLU=dt
Deadtime_NMDA=dt
Deadtime_GABAa=dt
Deadtime_GABAb=dt
// Cluster allocation proceedure
//--------------------------------------------------------------------------------------------------------
proc cluster_salloc() { local cluster_size, cluster_amount, b, b2
cluster_size=$2 // number of synapses per cluster
cluster_amount=$3 // number of clusters
sprint(tmpstr, "%s/cluster-sections", econ.data_dir) // open file to store the name of branches containing a cluster
vf = new File()
vf.wopen(tmpstr)
// For cluster_amount clusters, pick a different branch and put synapses on it
cluster_list = new SectionList()
for b=0,cluster_amount-1 {
$o1.pick_and_remove() // select a branch to put a cluster of synapses on
// $o1.pick() // to allow branches with multiple clusters
cluster_list.append() // store selected branches in a list
nseg = cluster_size // set number of segments = number of synapses in cluster
vf.printf("%s\n", secname()) // print branch name in file with cluster-section names
// sprint(tmpstr,"%s.v(0.5)", secname())
// if (b < 4) {
// addgraph(tmpstr,-70,0) // use to plot trace in current branch
// }
// distribute synapses uniformly along each branch in the cluster
for b2=1,cluster_size {
posn=(2*b2 -1)/(2*cluster_size) // define location for a new synapse
ampa[synapses_alloced]=new GLU(posn) // insert an AMPA mechanism at the above location
nmda[synapses_alloced]=new NMDA(posn) // insert an NMDA mechanism at the above location
// allocate mechanisms and plot on shape graph
salloc2(ampa[synapses_alloced],nmda[synapses_alloced],posn,1,splot,COLOR)
synapses_alloced = synapses_alloced + 1 // update the counter of AMPA/NMDA synapses allocated
}
}
// Randomly distribute remaining synapses on the remaining obliques
sprint(tmpstr2, "%s/random-sections", econ.data_dir)
vf2 = new File() // open file to store the name of branches with random
vf2.wopen(tmpstr2) // number of synapses on
for b=0, (all_synapses - cluster_size*cluster_amount)-1 {
$o1.pick() // pick a branch
nseg = cluster_size // set number of segments = number of synapses in cluster
vf2.printf("%s random_list.append()\n", secname()) // print branch name in file with random-sections names
ampa[synapses_alloced]=new GLU(0.5) // insert an AMPA mechanism in the middle of the branch
nmda[synapses_alloced]=new NMDA(0.5) // insert an NMDA mechanism in the middle of the branch
// allocate mechanisms and plot on shape graph
salloc2(ampa[synapses_alloced],nmda[synapses_alloced],0.5,1,splot,COLOR+1)
synapses_alloced=synapses_alloced+1 // update the counter of AMPA/NMDA synapses allocated
}
// add a small fixed amount of inhibition on soma and trunk (one gabaa/gabab) per section in list
forsec inh_list {
posa = 0.5
posb = 0.5
gabaa[gaba_synapses] = new GABAa(posa)
gabab[gaba_synapses] = new GABAb(posb)
gmaxb = 3*gmaxb_default // explicitly define GABA_A and GABA_B conductances
gmaxa = 4.8*gmax_default // for these sections (don't use GABA/AMPA ratios)
ifsec soma_list {
gmaxb = 0.03*gmaxb_default // very little GABA_B at the soma
gmaxa = 6*gmax_default // more GABA_A at the soma
}
SALLOC_GABAa(gabaa[gaba_synapses],posa,0,gmaxa) // allocate synapses using the above conductances
SALLOC_GABAb(gabab[gaba_synapses],posb,0,gmaxb)
//splot.point_mark(gabab[gaba_synapses],COLOR+2)
gaba_synapses = gaba_synapses + 1 // update GABA synapse counter
}
forsec taft_list {
//add one GABAa/GABAb synapse on the middle of the selected branch located > 300um
posa = 0.5
posb = 0.5
gabaa[gaba_synapses] = new GABAa(posa) // insert a GABA_A mechanism at the above location
SALLOC_GABAa(gabaa[gaba_synapses],posa,1,gmax_default) // allocate mechanism using the AMPA-deduced conductance
gabab[gaba_synapses] = new GABAb(posb) // insert a GABA_B mechanism at the above location
SALLOC_GABAb(gabab[gaba_synapses],posb,1,gmaxb_default) // allocate mechanism using the AMPA-deduced conductance
gaba_synapses = gaba_synapses + 1
}
vf.close()
vf2.close()
} // End of cluster allocation proceedure
//--------------------------------------------------------------------------------------------------------
hertz=50 // frequency of stimulation for AMPA/NMDA synapses
gaba_hertz=50 // frequency of stimulation for GABA synapses
perio=0 // spike trains for each synapse are NOT periodic
econ.xopen_library("Terrence","basic-graphics") // open library file for graphics
addgraph_2("soma.v(0.5)",0,tstop,-72,20) // make a graph of somatic voltage trace
//addgraph_2("apical_dendrite[60].v(0.5)",0,tstop,-72,20) // make a graph of dendrite 60 voltage trace
//addgraph_2("apical_dendrite[95].v(0.5)",0,tstop,-72,20) // make a graph of dendrite 95 voltage trace
apical_tipl=new SectionList() // make a list of all apical oblique dendrites
forsec apical_tip_list {
apical_tipl.append()
}
forsec apical_tip_list_addendum {
apical_tipl.append()
}
// make a list of taft sections where a fixed amount of inhibition will be inserted
taft_list=new SectionList()
apical_dendrite[73] taft_list.append() // 1 degree 334.84
apical_dendrite[82] taft_list.append() // 0 degree 360.28
apical_dendrite[86] taft_list.append() // 2nd degree 419.68
apical_dendrite[93] taft_list.append() // 2nd degree 468.60
apical_dendrite[97] taft_list.append() // 1st degree 445.90
apical_dendrite[105] taft_list.append() // 0 degree 425.58
// make a list of soma/trunk sections where a fixed amount of inhibition will be inserted
inh_list=new SectionList()
forsec "soma" {
inh_list.append()
}
apical_dendrite[6] inh_list.append()
apical_dendrite[26] inh_list.append()
apical_dendrite[48] inh_list.append()
apical_dendrite[64] inh_list.append()
apical_dendrite[81] inh_list.append()
apical_dendrite[104] inh_list.append()
for eiter=0,experiments-1 { // for all different cluster sizes
// define data directory
sprint(econ.data_dir, "data/Syns=%d-Synch=%d-BACK_GROUND=%d-Taft/T600N=%d-NoCL=%d-SiCl=%d",all_synapses, synch, BACK_GROUND, all_synapses, init_cluster_number, init_cluster_size)
//econ.data_dir = "data/MSEPredictions/T600N=50-NoCL=5-SiCL=10"
print " ------------------------ Experiment ", eiter
cluster_n = exp_ta[eiter] // get the cluster number
// set directory name
sprint(econ.data_dir,"%s/N=%d-CLn=%d-hertz=%d-experiment-%d",econ.data_dir, all_synapses,cluster_n,hertz,eiter)
sprint(econ.syscmd, "mkdir -p %s", econ.data_dir)
system(econ.syscmd) // make a directory for this experiment
for runs=0, 9 { // do this experiment 10 times (10 different synapse distributions for the same number of clusters)
splot=new Shape()
COLOR=6+runs
rpid=new Random(runs+eiter)
PID=int(rpid.uniform(1,10000)) // random seed for AMPA/NMDA synapses
PID=-PID // choose branchwise
rpid=new Random(runs+eiter+1)
PIDh=int(rpid.uniform(1,10000)) // random seed for GABA synapses
lo=0 // smallest distance of selected obliques from soma
hi=10000 // maximum distance of selected obliques from soma
// make a band (list) of randomly selected obliques within lo and hi microns from soma
band=new SynapseBand(apical_tipl,lo,hi,actual_resolution,desired_resolution,PID)
synapses_alloced=0 // initialize AMPA/NMDA synapse counter
gaba_synapses=0 // initialize GABA synapse counter
//BACKGROUND stimulation
// randomly distribute exitatory and inhibitory background synapses
if (BACK_GROUND) {
for i=0, back_syne -1 {
band.pick() // pick a branch
nseg = 10
rpid=new Random(i+runs)
pos1=int(rpid.uniform(0,10))/10 // random selection of synapse location
ampa_bg[i]=new GLU(pos1) // insert an AMPA mechanism in the middle of the branch
nmda_bg[i]=new NMDA(pos1) // insert an NMDA mechanism in the middle of the branch
salloc2(ampa_bg[i],nmda_bg[i],pos1,1,splot,COLOR+1)
}
for i=0, back_syni -1 {
band.pick() // pick a branch
nseg = 10
rpid=new Random(2*i+3+runs)
pos2=int(rpid.uniform(0,10))/10 // random selection of synapse location
gabaa_bg[i] = new GABAa(pos2)
SALLOC_GABAa(gabaa_bg[i],pos2,1,gmax_default) // allocate mechanism using the AMPA-deduced conductance
splot.point_mark(gabaa_bg[i],COLOR+1) // plot on shape graph
gabab_bg[i] = new GABAb(pos2) // insert a GABA_B mechanism at the above location
}
}
printf("cluster_salloc(band, cluster_size, cluster_number)\n") // display cluster allocation proceedure start
cluster_salloc(band,init_cluster_size,exp_ta[eiter]) // run cluster allocation proceedure
GABA_flag = 0 // don't make both AMPA/NMDA and GABA trains in shiftsyn_init
temporal_offset=0 // no temporal offset for spike train initiation
// create the stimulation trains for AMPA & NMDA synapses
econ.xopen_library("Terrence","shiftsyn-initA")
shiftsyn_init(all_synapses,tstop,dt,hertz,synch,perio,PID,temporal_offset,GABA_flag,"ampa","nmda")
// Create the stimulation trains for GABA synapses
econ.xopen_library("Terrence","GABA_shiftsyn")
gaba_shift(gaba_synapses,tstop,dt,gaba_hertz,synch,perio,PIDh,temporal_offset,"gabaa","gabab")
//BACKGROUND stimulation
if (BACK_GROUND) {
// create the stimulation trains for AMPA & NMDA background synapses
econ.xopen_library("Terrence","shiftsyn-init_bg")
shiftsyn_init(back_syne,tstop,dt,5,synch,perio,PID+2,temporal_offset,GABA_flag,"ampa_bg","nmda_bg")
// Create the stimulation trains for GABA background synapses
econ.xopen_library("Terrence","GABA_shiftsyn_bg")
gaba_shift(back_syni,tstop,dt,5,synch,perio,PIDh+5,temporal_offset,"gabaa_bg","gabab_bg")
}
vrec=new Vector(tstop/dt) // prepare to record somatic voltage
vrec.record(&soma.v(0.5))
finitialize(v_init) // initialize and run
fcurrent()
run()
// print the synapse distribution on the cell and the respective branch names to be used later
// to estimate the cell firing rate using various mathematical models (linear, sigmoidal, quadratic etc).
// Model predictions are calculated (with matlab scripts) using the alpha branch coefficients estimated from
// single-branch-stimulation experiments. The alpha coefficent for each branch is just the mean somatic
// depolarization in response to 50 Hz stimulation of increasing numbers of synapses (1-10) within
// each side branch
// sort the randomly selected sections and count the number of synapses in each one.
// Save the numbers (of synapses) in random_syn using the sorted order
sprint(Fc, "%s/random_syn", econ.data_dir )
sprint(econ.syscmd, "sort < %s | uniq -c | awk '{print $1}' > %s", tmpstr2, Fc )
system(econ.syscmd)
// sort and save the list of randomly selected sections in random-sections
strdef tmpstr3
sprint(tmpstr2, "%s/random-sections", econ.data_dir)
sprint(tmpstr3, "%s/random-sections-sorted", econ.data_dir)
sprint(econ.syscmd, "sort < %s | uniq > %s", tmpstr2, tmpstr3)
system(econ.syscmd)
// read the sorted list of randomly selected sections
random_list = new SectionList()
xopen(tmpstr3)
// read the number of synapses per randomly selected section
vf=new File()
vf.ropen(Fc)
input.scanf(vf)
vf.close()
// store the input configuration in a file, to be read from matlab scripts
vf=new File()
sprint(tmpstr, "%s/input_profile", econ.data_dir)
vf.aopen(tmpstr)
forsec apical_tipl {
sprint(tmpstr, "%s", secname())
flag = 0
forsec cluster_list {
ifsec tmpstr {
x = init_cluster_size
vf.printf("%d\t", x)
flag = 1
break
}
}
if (flag==0) {
i = 0
forsec random_list {
ifsec tmpstr {
x = input.x[i]
vf.printf("%d\t", x)
flag = 1
break
}
i = i + 1
}
}
if (flag==0) {vf.printf("%d\t", 0)}
}
vf.printf("\n")
vf.close()
vf=new File()
sprint(tmpstr, "%s/%d_synapses", econ.data_dir, all_synapses) // define file to save actual cell firing rate
spikevec=new Vector()
spikevec.spikebin(vrec,0)
// open file and print: spikes, time,
vf.aopen(tmpstr)
vf.printf("%d %5d\n", spikevec.sum(), tstop)
vf.close()
//sprint(tmpstr, "%s/syn_distribution-CL=%d.eps", econ.data_dir, cluster_n) // print the shape graph
//splot.printfile(tmpstr) // with current synapse distribution
// econ.xopen_library("Terrence","verbose-system")
// for i=0,windex {
// sprint(econ.tmp_str, "%s/graph-%d.eps",econ.data_dir, i)
// win[i].printfile(econ.tmp_str)
// }
} // end of runs
} // end of experiments to run
Simulation Platform