Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)

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Accession:138379
"Coordination of neocortical oscillations has been hypothesized to underlie the "binding" essential to cognitive function. However, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown. We hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network. We simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs. ..."
Reference:
1 . Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 5:19 [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 L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Oscillations; Synchronization; Laminar Connectivity;
Implementer(s): Lytton, William [bill.lytton at downstate.edu]; Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Gaba; Glutamate;
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fdemo
readme.txt
intf6_.mod
misc.mod *
nstim.mod *
stats.mod *
vecst.mod
col.hoc
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc
finish_run.hoc
grvec.hoc *
init.hoc *
labels.hoc *
local.hoc *
misc.h
mosinit.hoc
network.hoc
nload.hoc
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
python.hoc *
pywrap.hoc *
run.hoc
setup.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
xgetargs.hoc *
                            
// $Id: stats.hoc,v 1.3 2008/12/22 03:50:27 samn Exp $ 


//based on code from:
//http://pdos.csail.mit.edu/grid/sim/capacity-ns.tgz/capacity-sim/new-ns/
//hoc template that allows sampling from a pareto power law distribution 
//specified with objref rd
//rd = new rdmpareto($1=avg,$2=shape,[$3=seed])
//then picking values with .pick , or assigning to a vec with assignv(vec)
begintemplate rdmpareto
public avg,shape,rd,seed,pick,repick,paretoc,pareto5,assignv,reset,pareto4,pareto3
double avg[1],shape[1],seed[1]
objref rd
proc init () {
  avg=$1 shape=$2
  if(numarg()>2)seed=$3 else seed=1234
  rd=new Random()
  rd.ACG(seed)
}
proc reset () {
  rd.ACG(seed)
}
func paretoc () { local scale,shape,U
  scale=$1 shape=$2 U = rd.uniform(0,1)
  return scale * (1.0/ U^(1/shape) )
}
func pareto5 () { local avg,shape
  avg=$1 shape=$2
  return paretoc( avg * (shape -1)/shape, shape)
}
func pareto4 () { local alpha,u
  alpha=$2
  u = 1 - rd.uniform(0,1)
  return $1 + 1 / u^(1/alpha)
}
func pareto3 () { local x,z,b,a
  b = avg // 1 //min value
  a = shape // 10
  x = rd.uniform(0,1)
  z = x^-1/a
  return 1 + b * z
}
func pick () {
  return pareto5(avg,shape)
}
func repick () {
  return pick()
}
func assignv () { local i localobj vi
  vi=$o1 
  for i=0,vi.size-1 vi.x(i)=pick()
}
endtemplate rdmpareto

func skew () { local a,ret localobj v1
  a=allocvecs(v1)
  $o1.getcol($s2).moment(v1)
  ret=v1.x[4]
  dealloc(a)
  return ret
}

func skewv () { localobj v1
  v1=new Vector(5)
  $o1.moment(v1)
  return v1.x(4)
}


//** test rsampsig
objref vIN0,vIN1,vhsout,myrdm,vrs,VA
R0SZ=30000//size of group 0
R1SZ=30000//size of group 1
RPRC=100 // # of trials (combinations)
RS0M=0 //mean of group 0
RS1M=0 //mean of group 1
RS0SD=1 //sdev of group 0
RS1SD=1 //sdev of group 1
proc rsi () {
  if(myrdm==nil) myrdm=new Random()  
  {myrdm.normal(RS0M,RS0SD) vIN0=new Vector(R0SZ) vIN0.setrand(myrdm)}  
  {myrdm.normal(RS1M,RS1SD) vIN1=new Vector(R1SZ) vIN1.setrand(myrdm)}
  vhsout=new Vector(vIN0.size+vIN1.size)
  if(RPRC>1){
    vrs=new Vector(RPRC)
  } else {
    vrs=new Vector(combs_stats(R0SZ+R1SZ,mmax(R0SZ,R1SZ))*RPRC)
  }
  VA=new Vector()  VA.copy(vIN0) VA.append(vIN1)
}
func hocmeasure () {
  hretval_stats=vhsout.mean
  return vhsout.mean
}
func compfunc () {
  if(verbose_stats>1) printf("$1=%g,$2=%g\n",$1,$2)
  hretval_stats=$1-$2
  return hretval_stats
}
onesided=0
nocmbchk=1
pval=tval=0
func testrs () { local dd localobj str
  if(numarg()>0)dd=$1 else dd=1
  str=new String()
  rsi()
  vhsout.resize(vIN0.size+vIN1.size)
  pval=vrs.rsampsig(vIN0,vIN1,RPRC,"hocmeasure","compfunc",vhsout,onesided,nocmbchk)
  tval=ttest(vIN0,vIN1)
  if(dd){
    sprint(str.s,"p(abs(m0-m1))>%g=%g, t=%g, e=%g",abs(vIN0.mean-vIN1.mean),pval,tval,abs(pval-tval)/tval)
    {ge() ers=0 clr=1 hist(g,VA) clr=2  hist(g,vIN0) clr=3  hist(g,vIN1) g.label(0,0.95,str.s)}
    sprint(str.s,"m0=%g, m1=%g, n0=%g, n1=%g, s0=%g, s1=%g",vIN0.mean,vIN1.mean,vIN0.size,vIN1.size,vIN0.stdev,vIN1.stdev)
    g.label(0.0,0.0,str.s)
    sprint(str.s,"m0-m1=%g",vIN0.mean-vIN1.mean)
    g.label(0,0.9,str.s)
    g.exec_menu("View = plot")
  }
  printf("pval=%g, tval=%g, err=%g\n",pval,tval,abs(pval-tval)/tval)
  return pval
}

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