CA3 Network Model of Epileptic Activity (Sanjay et. al, 2015)

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Accession:186768
This computational study investigates how a CA3 neuronal network consisting of pyramidal cells, basket cells and OLM interneurons becomes epileptic when dendritic inhibition to pyramidal cells is impaired due to the dysfunction of OLM interneurons. After standardizing the baseline activity (theta-modulated gamma oscillations), systematic changes are made in the connectivities between the neurons, as a result of step-wise impairment of dendritic inhibition.
Reference:
1 . Sanjay M, Neymotin SA, Krothapalli SB (2015) Impaired dendritic inhibition leads to epileptic activity in a computer model of CA3. Hippocampus 25:1336-50 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Extracellular;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s): HCN1; HCN2;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Oscillations; Pathophysiology; Epilepsy; Brain Rhythms;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Sanjay, M [msanjaycmc at gmail.com];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; GabaA; AMPA; NMDA;
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SanjayEtAl2015
readme.html
CA1ih.mod *
CA1ika.mod *
CA1ikdr.mod *
CA1ina.mod *
caolmw.mod *
capr.mod *
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kapyrkop.mod *
kcaolmw.mod *
kcpr.mod *
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kdrpyrkop.mod *
misc.mod *
MyExp2Syn.mod *
MyExp2SynAlpha.mod *
MyExp2SynBB.mod *
MyExp2SynNMDA.mod *
MyExp2SynNMDABB.mod *
nafbwb.mod *
nafolmkop.mod *
nafpr.mod *
nafpyrkop.mod *
stats.mod
vecst.mod *
wrap.mod *
aux_fun.inc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
Epileptic Activity.png
geom.hoc *
geom.py *
grvec.hoc *
init.hoc *
labels.hoc *
local.hoc *
misc.h *
mosinit.py
network.py *
networkmsj.py
nqs.hoc *
nqs_utils.hoc *
nrnoc.hoc *
params.py
pyinit.py *
run.py
simctrl.hoc *
stats.hoc *
syncode.hoc *
xgetargs.hoc *
xtmp
                            
// $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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