Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)

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Accession:146949
"... We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. ... By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping."
Reference:
1 . Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GM, Lytton WW (2014) Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput 26:1239-62 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex M1 pyramidal intratelencephalic L2-5 cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Laminar Connectivity;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Shepherd, Gordon MG [g-shepherd at northwestern.edu]; Chadderdon, George [gchadder3 at gmail.com]; Kerr, Cliff [cliffk at neurosim.downstate.edu];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex M1 pyramidal intratelencephalic L2-5 cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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src
README
infot.mod *
intf6.mod *
intfsw.mod *
matrix.mod
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
boxes.hoc *
col.hoc
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc *
gcelldata.hoc
gmgs102.nqs
grvec.hoc *
infot.hoc *
init.hoc
intfsw.hoc *
labels.hoc *
load.py
local.hoc *
main.hoc
misc.h *
miscfuncs.py
network.hoc
neuroplot.py *
nload.hoc
nqs.hoc *
nqsnet.hoc
nrnoc.hoc *
params.hoc
run.hoc
samutils.hoc *
saveoutput.hoc
saveweights.hoc
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
wdmaps2.nqs
xgetargs.hoc *
                            
// $Id: updown.hoc,v 1.116 2010/09/09 15:56:12 samn Exp $

print "Loading updown.hoc..."

printStep=0.1
CREEP_UPDOWN=1
NOV_UPDOWN=1

{declare("test_do_graphs", 1)}
{declare("drawlr", 1, "drawth", 1,"drawscale",0,"shapesz",15,"flipit",0)}
{declare("minthreshlx", 0, "maxthreshlx",9e3*printStep)}
{declare("black",1,"red",2,"blue",3,"green",4)}

// EXAMPLE USAGE
// {gvnew(-2) panobj.read_vfile("/u/billl/nrniv/sync/data/05nov02.501/v05nov02.1000")}
// panobj.rv(7)
// vec.copy(panobj.vrtmp)
// vec.mul(-1)
// gg(vec,printStep)
// objref output
// output=updown(vec)
// output.pr(10)

// updown(vec[,nq,#CUTS,LOGCUT,MIN]) -- use updown() to find spikes
// LOC(0) PEAK(1) WIDTH(2) BASE(3) HEIGHT(4) START(5) SLICES(6) SHARP(7) INDEX(8) FILE(9)
// other options
pos_updown=1 // set to 1 to move whole curve up above 0
maxp_updown=0.95 // draw top sample at 95% of max 
minp_updown=0.05 // draw bottom sample at 5% of max
over_updown=1    // turn over and try again if nothing found
allover_updown=0 // turn over and add these locs (not debugged)
verbose_updown=0 // give messages, can also turn on DEBUG_VECST for messages from updn()
{declare("dynamic_th",0)}

//** updown - calls Vector updown function and returns NQS with spikes/bumps
obfunc updown () { local a,ii,npts,logflag,min,x,sz,midp localobj bq,cq,v1,v2,v3,bb,tl,vtmp
  if (verbose_updown) printf("MAXTIME appears to be %g (printStep=%g)\n",$o1.size*printStep,printStep)
  npts=10 // n sample locations
  tl=new List()
  bq=new NQS("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") 
  if (numarg()>1) cq=$o2 else cq=new NQS()
  if (cq.m!=10) { cq.resize(0) 
    cq.resize("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") }
  if (numarg()>2) npts=$3
  if (numarg()>3) logflag=$4 else logflag=0
  if (numarg()>4) {
    if (npts!=$o5.size) printf("Correcting npts from %d to %d\n",npts,npts=$o5.size)
    if ($o5.ismono(1)) $o5.reverse
    if (! $o5.ismono(-1)) {printf("updown: Arg 5 (%s) must be monotonic\n",$o5) return}
  }
//  if (numarg()>5) dynflag=$6 else dynflag=0

  bq.listvecs(bb)
  bq.pad(5000)
  a=allocvecs(npts+3,2e4)
  v1=mso[a+0] v2=mso[a+1] v3=mso[a+2]
  for ii=0,npts-1 tl.append(mso[ii+a+3])
  v1.copy($o1)
  if (pos_updown) {
    min=v1.min
    v1.sub(min) // make it uniformly positive
  } else min=0
  if (numarg()>4) v2.copy($o5) else {
    if(logflag==2){ //"double-log" spacing

      midp = v1.max * 0.5 //50% of max is center of log axis in vertical direction

      // 1/2 of lines btwn max and midpoint
      vtmp=new Vector()
      vtmp.indgen(2,2+npts/2-1,1)
      vtmp.log()
      vtmp.scale(maxp_updown*v1.max,midp)

      v2.append(vtmp)

      // 1/2 of lines btwn min and midpoint
      vtmp.indgen(2,2+npts/2-1,1)
      vtmp.log()
      vtmp.scale(minp_updown*v1.max,midp)

      v2.append(vtmp)

      //make sure they're monotonically increasing
      v2.sort()
      v2.reverse()

    } else {
      v2.indgen(2,2+npts-1,1)   // sampling at npts points, start at 2 to avoid log(1)=0
      if (logflag) v2.log() // log sampling
      v2.scale(-maxp_updown*v1.max,-minp_updown*v1.max) v2.mul(-1)
    }
  }

  if(dynamic_th) { //allocate memory so updown can use th
    v2.resize(v1.max()+1)
    v2.resize(0)
    printf("dynamic_th\n")
  }

  vec0.copy(v2) // <--------- whats this for? copies threshold lines to global vec0

  //v2 = thresh
  v1.updown(v2,tl,bb)

  if (pos_updown) { bq.v[1].add(min) bq.v[3].add(min) }
  cq.append(bq)
  sz=bq.size(1)
  if (allover_updown) { // do it upside down as well
    v1.mul(-1) // v2 will be upside-down
    if (pos_updown) {min=v1.min v1.sub(min)}
    v1.updown(v2,tl,bb)
    bq.v[8].add(sz) bq.v[4].mul(-1) // turn HEIGHT upside down
    cq.append(bq)
  } else if (over_updown && sz==0) { // turn it over an try again
    print "updown() checking upside-down"
    v1.mul(-1) // v2 will be upside-down
    v1.updown(v2,tl,bb)
  } 
  for case(&x,0,2,5) cq.v[x].mul(printStep)
  nqsdel(bq)
  dealloc(a)
  return cq
}

//** testupdown($o1=input vector,$2=num slices,$3=logarithmic-spaced slices,$o4=thresholds slices locations)
// runs updown on input vector and returns an NQS with detected spikes/bumps
obfunc testupdown (){ local idx,left,right,x,a localobj vtmp,output,vec
  {a=allocvecs(vec) output = new NQS() vec.copy($o1)}
  if(flipit) vec.mul(-1)
  vec.sub(vec.min)
  if(numarg()>3){
    updown(vec,output,$2,$3,$o4)
  } else if(numarg()>2){
    updown(vec,output,$2,$3)
  } else{
    updown(vec,output)
  }
  if(test_do_graphs){ if(g==nil) gg()
    gg(vec,printStep,black,3) //5 is for thick lines       
    output.v[1].mark(g,output.v,"o",shapesz,red,1) //draw circles at top of peaks
    if(drawlr) for(idx=0;idx<output.v.size;idx+=1){
      left = output.v[5].x(idx)
      right = left+output.v[2].x(idx)
      g.mark(left, vec.x(left/printStep), "t",shapesz,blue ,1)
      g.mark(right,vec.x(right/printStep),"s",shapesz,green,1)
    }       
     // draw horizontal lines for slices
    if(drawth) for vtr(&x,vec0) drline(minthreshlx,x,maxthreshlx,x,red,1)
  } 
  dealloc(a)
  return output
}

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