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

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This model is an extension of a model (<a href="">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.
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;
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]; Neymotin, Sam [samn at]; Kerr, Cliff [cliffk at];
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;
infot.mod *
intf6_.mod *
intfsw.mod *
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
decmat.hoc *
decnqs.hoc *
default.hoc *
drline.hoc *
infot.hoc *
local.hoc *
misc.h *
ratlfp.dat *
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
xgetargs.hoc *
// $Id: infot.hoc,v 1.43 2009/12/04 01:25:55 samn Exp $ 

print "Loading infot.hoc..."

if(!installed_infot) {install_infot()}

//* tentropsig(v1,v2,nshuf,nbins,twoway,[xpast,ypast,hval])
//significance test of transfer entropy using shuffling
//returns (te - tes) / sds , where te is transfer entropy, tes is transfer entropy
//of shuffled data, sds is std-dev of transfer entrop of shuffled data
//should only accept as significanat values > 4-6
func tentropsig () { local nshuf,i,xp,yp,hv,nbins,te localobj v1,v2,vo
  v1=new Vector() v2=new Vector() vo=new Vector(1)
  v1.copy($o1) v2.copy($o2) nshuf=$3 nbins=$4
  if(numarg()>4) xp=$5 else xp=1
  if(numarg()>5) yp=$6 else yp=2
  if(numarg()>6) hv=$7 else hv=0
  if(1||verbose_infot>2) printf("te=%g,sig=%g\n",te,vo.x(0))
  return vo.x(0)

//** mutinfbshufv(v1,v2,[nshuf,nbins])
//return vector with mutual information from shuffled v1,v2
//used for significance test , i.e. : ((miorig - mishufmean) / mishufstdev) > 2
obfunc mutinfbshufv () { local nshuf,nbins,i localobj v1,v2,ve
  v1=new Vector() v2=new Vector() ve=new Vector()
  v1.copy($o1) v2.copy($o2)
  if(numarg()>2) nshuf=$3 else nshuf=20
  if(numarg()>3) nbins=$4 else nbins=10
  for i=0,nshuf-1 {
    v1.shuffle() v2.shuffle()
  return ve

//** mutinfbsig(v1,v2,[nshuf,nbins])
//get significance of mutual information, should be at least > 2
func mutinfbsig () { local nshuf,nbins,st localobj ve
  if(numarg()>2) nshuf=$3 else nshuf=20
  if(numarg()>3) nbins=$4 else nbins=10
  if(st<=0) st=1
  return ($o1.mutinfb($o2,nbins) - ve.mean) / st

//** tentropspksig(v1,v2,nshuffles)
//get significance of tentropspks using shuffling
//returns (TE - AvgTEShuffle) / StdDevTEShuffle
func tentropspksig () { local nshuf,i,xp,yp,hv,nbins,te,sd localobj v1,v2,ve
  v1=new Vector() v2=new Vector() ve=new Vector()
  v1.copy($o1) v2.copy($o2) nshuf=$3
  for i=0,nshuf-1 {
    v1.shuffle()     ve.append(v1.tentropspks(v2))
  if(verbose_infot>2) printf("te=%g,ve.mean=%g,ve.stdev=%g\n",te,ve.mean,ve.stdev)
  if(verbose_infot>2) ve.printf
  return (te-ve.mean)/sd

//* normte() get normalized transfer entropy using tentropspks in output vector vo
//vo.x(0)=transfer entropy of $o1->$o2
//vo.x(2)=normalized transfer entropy in 0,1 range
//$3==number of shuffles
//$o1,$o2 should both have same size and non-negative values. this func is meant for time-binned spike train data
obfunc normte () { local a localobj ve,vo
  a=allocvecs(ve) vo=new Vector()
  nshuf=$3 vrsz(3+nshuf,vo) 
  if(verbose_infot>2) vo.printf
  if(vo.x(1)<=0 && verbose_infot>0){printf("WARNING H(X2F|X2P)==%g<=0\n",vo.x(1)) vo.x(1)=1 }
  if (nshuf>0) {
    if (ve.mean!=vo.x[2]) printf("normte ERRA\n")
  return vo

//* GetTENQ() get an nqs with useful transfer entropy info
obfunc GetTENQ () { local te01,te10,pf01,pf10 localobj nqte,vo1,vo2
  if(numarg()>3) nqte=$o4
  if(nqte==nil) {
    nqte=new NQS("from","to","TE","NTE","HX2|X2P","prefdir","TEshufavg","TEshufstd","sig")
  } else nqte.clear()
  if(te01>0 || te10>0) {
  } else {
  return nqte

//** prefdte() get preferred direction of transfer entropy
//$o1=vec 1, $o2=vec 2, $3 = # of times to shuffle
func prefdte () { local nshuf,a,te01,te10,pfd localobj v1,v2,vtmp
  v1.copy($o1) v2.copy($o2) nshuf=$3 vtmp.resize(3)
  return pfd

//** mkchist() averages entries in window into disc values and returns in new output vec
//$o1=input vec,$2=win size
obfunc mkchist () { local idx,eidx,wsz localobj vin,vout
  vin=$o1 wsz=$2 vout=new Vector() 
  vout.resize(1+vin.size/wsz) vout.resize(0)
  for(idx=0;idx<=vin.size;idx+=wsz) {
    if(eidx>idx) vout.append( int(vin.mean(idx,eidx)) )
  return vout

//get magnitude of difference in a preferred direction - just abs of diff, but if theyre both neg, return 0
//$1 = nTE_X->Y
//$2 = nTE_Y->X
func prefdmag () { local n1,n2,s
  n1=$1 n2=$2
  if(n1>0 && n2<=0) return n1-n2 //n1 is relatively strong
  if(n2>0 && n1<=0) return n2-n1 //n2 is relatively strong
  if(n2<0 && n1<0) return 0      //both are weak
  return abs(n1-n2)              //both are weak positive

//** simple test for nte
for i=0,1 {
  vb[i]=new Vector()
  vs[i]=new Vector()

//mkspktrain(Random,rate,tmax) -- make a spike train with specified rate,tmax
//Random obj must be initialized
obfunc mkspktrain () { local tmax,rate,t,dt,intt localobj rdp,vs
  rdp=$o1 rate=$2 tmax=$3
  t = 0
  vs=new Vector()
  while(t<=tmax) {
    dt = rdp.poisson(intt)
    t += dt
  return vs

//make random spikes with frequency $1, tmax=$2, offset for spikes=$3, alpha=$4 -- ratio of spikes from
//vs[0] that get placed in vs[1] 
//spikes in vs[0] are randomly picked, spikes in vs[1] are same as in vs[0] but shifted forward by $3 offset
//so vs[0] 'drives' vs[1], or can be used to predict it, but vs[1] cant be used to predict vs[0]
proc mkspks () { local tmax,rate,t,dt,intt,off,i,alpha localobj rdp
  rate=$1 tmax=$2 off=$3 
  if(numarg()>3)alpha=$4 else alpha=1
  rdp=new Random()
  for i=0,1 vs[i].resize(0)
  if(alpha < 1.0) {
    for vtr(&t,vs[0]) if(rdp.uniform(0,1) <= alpha) vs[1].append(t+off)
  } else {
//test nTE : nTE of X0 -> X1 should be much higher than nTE of X1 -> X0
//optional $1=offset == offset to shift spikes by, in ms
//optional $2=rate == rate of spikes, in Hz
//optional $3=bin size , in ms
//optional $4=alpha == ratio of spikes of X0 that get placed in X1 with offset
//optional $5=max time, in ms
func testnte () { local a,i,bisv,maxt,alpha,off,rate,binsz,dur  localobj nqt,nqout
  if(numarg()>0)off=$1 else off=10
  if(numarg()>1)rate=$2 else rate=50
  if(numarg()>2)binsz=$3 else binsz=10
  if(numarg()>3)alpha=$4 else alpha=1
  if(numarg()>4)dur=$5 else dur=10000
  bisv=binmin_infot binmin_infot=0
  print "output should be close to:\n\t0 1 0.6707 0.9975 0.6707 0.9407 0.003435 0.001672 399.1"
  print "\t1 0 0.02183 0.03049 0.6685 -0.9407 0.002828 0.001442 13.17"
  for i=0,1 vb[i].hist(vs[i],0,(maxt+binsz-1)/binsz,binsz)
nqout=new NQS("X1","X2")
  return 1

//get kernel smoothed prob distrib in an nqs
//$o1=input vector
//$2=increment in x , smaller values mean finer resolution
//$3=bandwidth - higher means smoother output
// $4=min value in output, $5=max value in output
obfunc khist () { local min,max,inc,h,x,i,s localobj vx,vy,nq,vin
  if(numarg()>1)inc=$2 else inc=0.1
  if(numarg()>2)h=$3 else h=vin.getbandwidth()
  if(numarg()>3)min=$4 else min=vin.min()
  if(numarg()>4)max=$5 else max=vin.max()
  {vx=new Vector() vy=new Vector()}
  for vtr(&x,vx,&i) vy.x(i) = vin.kprob1D(h,x)
  if(s!=0) vy.div(vy.sum)
  nq=new NQS("x","y")
  return nq

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]

References and models cited by this paper

References and models that cite this paper

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(38 refs)