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: filtutils.hoc,v 1.11 2011/11/07 21:37:48 samn Exp $ 

print "Loading filtutils.hoc..."

//* mkgauss(vector,average,standard-dev)
proc mkgauss () { local i,x,sz,mu,sd localobj vin
  vin=$o1 sz=vin.size mu=$2 sd=$3
  for vtr(&x,vin,&i) vin.x(i) = exp( -(x-mu)^2 / (2*sd*sd) )
  vin.mul( 1 / (sd*sqrt(2*PI)) )
}

//* mktriangwin(vec,size - should be odd,[skip the wraparound])
proc mktriangwin () { local i,j,sz localobj vin
  vin=$o1 vin.resize($2)
  vin.x(int($2/2))=1
  j=1 sz=1/(vin.size/2)
  for (i=int($2/2)-1;i>=0;i-=1) {
    vin.x(i)=j
    j-=sz
  }
  j=1
  for i=int($2/2)+1,vin.size-1 {
    vin.x(i)=j
    j-=sz
  }
  vin.div(vin.sum)
  if(numarg()>2) return
  vin.wraparound(vin.size)
}

//* mkgaussfilt(vec,stdev[,vx])
proc mkgaussfilt () { local sd,minx,maxx,dx,a localobj vin,vx
  vin=$o1 sd=$2
  if(numarg()>2) {vx=$o3 vin.resize(0) vin.copy(vx)}
  mkgauss(vin,0,sd)
  vin.wraparound(vin.size)
  vin.div(vin.sum)
  dealloc(a)
}

//* dofilt(vsignal,vwindow) - filters with convlv
proc dofilt () { local a,i localobj vsig,vwin,vtmp
  a=allocvecs(vtmp) vsig=$o1 vwin=$o2 sz=vsig.size
  vtmp.convlv(vsig,vwin)
  vsig.copy(vtmp)
  vsig.resize(sz) // make sure size doesn't change
  dealloc(a)  
}

//* triangfilt(vin,filtsize) - run a triangle filter
proc triangfilt () { local a localobj vin,vwin
  vin=$o1
  a=allocvecs(vwin)
  mktriangwin(vwin,$2) // make the window
  dofilt(vin,vwin) // do the filtering
  dealloc(a)
}

//* boxfilt(vin,filtsize) - run a box(moving average) filter
proc boxfilt () { local a localobj vin,vwin
  vin=$o1
  a=allocvecs(vwin)
  {vwin.resize($2) vwin.fill(1) vwin.div(vwin.size)} // make the window
  dofilt(vin,vwin) // do the filtering
  dealloc(a)
}

//* gaussfilt(vin,stdev,vx) - run a gaussian filter - vx is x-values used to make gaussian
proc gaussfilt () {  local a,sd localobj vin,vwin,vx
  vin=$o1 sd=$2 vx=$o3
  a=allocvecs(vwin)
  mkgaussfilt(vwin,sd,vx) // make the window
  dofilt(vin,vwin) // do the filtering
  dealloc(a)
}

//* myfilt(code,vec) - code:0=gauss,1=triangle,2=box
proc myfilt () { local a localobj vx
  if($1==0) {
    a=allocvecs(vx)
    vx.indgen(-3,3,.03)
    gaussfilt($o2,stdg,vx)
  } else if($1==1) {
    triangfilt($o2,winsz)
  } else if($1==2) {
    boxfilt($o2,winsz)
  }
}

//* resample(vec,new size) - resample a vec to new size using linear interpolation
proc resample(){ local newsz,idxdest,idxsrc,val,fctr,frac localobj vtmp
  {vtmp=new Vector($2) fctr=$o1.size/$2  vtmp.x(0)=$o1.x(0) idxsrc=fctr}
  for(idxdest=1;idxdest<$2-1;idxdest+=1){
    idxsrc = idxdest * fctr
    frac = idxsrc - int(idxsrc)
    idxsrc = int(idxsrc)
    if(idxsrc+1>=$o1.size){
      vtmp.x(idxdest) = $o1.x(idxsrc)
      continue
    }
    val = (1-frac) * $o1.x(idxsrc) + frac * $o1.x(idxsrc+1)
    vtmp.x(idxdest) = val
  }
  {vtmp.x($2-1)=$o1.x($o1.size-1) $o1.resize($2) $o1.copy(vtmp)}
}

//* bandfilt(datavector,low frequency,high frequency[,window size for bandpass filter])
// bandpass filter using via FFT convolution, $o1 will be modified
func bandfilt () { local a,idx,hz,lohz,hihz,wsz localobj v1,vwin,v2
  if(!name_declared("INSTALLED_myfft")){printf("bandfilt ERRA: myfft.mod not installed!\n") return 0}
  {v1=$o1  lohz=$2  hihz=$3}
  if(numarg()>3)wsz=$4 else wsz=1025
  a=allocvecs(vwin,v2)
  vwin.resize(wsz)
  vwin.bpwin(lohz/sampr,hihz/sampr) //make bandpass windowed sinc
  vwin.wraparound(wsz) //wrap around for FFT convolution
  v2.convlv(v1,vwin)
  v1.copy(v2)
  dealloc(a)
  return 1
}

//* multfilt(inputvector,outputvector,vector of lowfrequencies,vector of highfrequencies)
//get superposition of multiple frequency bands, each pair in $o3,$o4 should be considered a band
func multfilt () { local a,i localobj vtmp,vin,vout,vlo,vhi
  if(!name_declared("INSTALLED_myfft")) {printf("multfilt ERRA: myfft.mod not installed!\n") return 0}
  a=allocvecs(vtmp)
  vin=$o1 vout=$o2 vlo=$o3 vhi=$o4
  for i=0,vlo.size-1 {
    vtmp.copy(vin)
    bandfilt(vtmp,vlo.x(i),vhi.x(i))
    if(i==0)vout.copy(vtmp) else vout.add(vtmp)
  }
  vout.resize(vin.size)
  dealloc(a)  
  return 1
}

//* nrnpsd(vector,samplingrate) - calculates PSD and returns as an NQS object
// uses NEURON spctrm function
obfunc nrnpsd () { local sampr localobj vec,nqp
  vec=$o1 sampr=$2
  nqp=new NQS("f","pow")
  nqp.v[1].spctrm(vec)
  nqp.v.indgen(0,sampr/2,(sampr/2)/nqp.v[1].size)
  nqp.v.resize(nqp.v[1].size)
  return nqp
}

//* mkprefftwin(outputvec,windowtype[,windowsize]) - make window for multiplying before running fft
proc mkprefftwin () { local pref,sz localobj vpre
  vpre=$o1 pref=$2
  if(numarg()>2) sz=$3 else sz=vpre.size
  vpre.resize(sz)
  if(pref==1) {
    vpre.blackmanwin()
  } else if(pref==2) {
    vpre.hanningwin()
  } else if(pref==3) {
    vpre.hammingwin()
  } else {printf("mkprefftwin ERRA: unknown window type!\n") return}
}

//* getspecnq(vec,sampr[,specty,prefilt])
obfunc getspecnq () { local sampr,specty,pref,a localobj vec,vpre,v2,nq
  vec=$o1 sampr=$2 nq=nil
  if(numarg()>2) specty=$3 else specty=0
  if(numarg()>3) pref=$4 else pref=0
  if(pref) { // pre-fft filtering
    a=allocvecs(vpre,v2)
    vpre.resize(vec.size)
    mkprefftwin(vpre,pref)
    v2.copy(vec)
    v2.mul(vpre)
    vec=v2
  }
  if(specty==0) nq=pypmtm(vec,sampr)
  if(specty==1) nq=pypsd(vec,sampr)
  if(specty==2) nq=matpmtm(vec,sampr)
  if(specty==3) nq=nrnpsd(vec,sampr)
  if(specty==4) nq=pybspow(vec,sampr)
  if(pref) dealloc(a)
  return nq
}


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