Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)

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Accession:141505
This model is an extension of a model (<a href="http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=138379">138379</a>) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.
Reference:
1 . Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (2012) Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex IEEE Transactions on Neural Systems & Rehabilitation Engineering 20(2):153-60 [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 V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell;
Channel(s): I Chloride; I Sodium; I Potassium;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Deep brain stimulation; Information transfer; Brain Rhythms;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Kerr, Cliff [cliffk at neurosim.downstate.edu];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; I Chloride; I Sodium; I Potassium; Gaba; Glutamate;
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neuroprosthesis
README
infot.mod *
intf6_.mod *
intfsw.mod *
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
batch.hoc
boxes.hoc
bsmart.py
col.hoc
comparecausality.py
comparerasters.py
declist.hoc
decmat.hoc *
decnqs.hoc *
decvec.hoc
default.hoc *
drline.hoc *
filtutils.hoc
flexinput.hoc
grvec.hoc
infot.hoc *
init.hoc
intfsw.hoc
labels.hoc
local.hoc *
misc.h *
mosinit.hoc
network.hoc
nload.hoc
nqs.hoc
nqsnet.hoc
nrnoc.hoc
params.hoc
pyhoc.py
ratlfp.dat *
run.hoc
runsim
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
xgetargs.hoc *
                            
// $Id: filtutils.hoc,v 1.6 2010/10/11 18:34:24 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)}
}

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