Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013)

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Accession:151282
" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih. Our model demonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and amplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards particular phases of the theta cycle, effected via Ih’s ability to set pyramidal excitability. ..."
Reference:
1 . Neymotin SA, Hilscher MM, Moulin TC, Skolnick Y, Lazarewicz MT, Lytton WW (2013) Ih Tunes Theta/Gamma Oscillations and Cross-Frequency Coupling In an In Silico CA3 Model PLoS ONE 8(10):e76285 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal cell; Hippocampus CA3 basket cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s): I Na,t; I A; I K; I K,leak; I h; I K,Ca; I Sodium; I Potassium;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Glutamate;
Gene(s): HCN1; HCN2;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Oscillations; Brain Rhythms; Conductance distributions; Multiscale;
Implementer(s): Lazarewicz, Maciej [mlazarew at gmu.edu]; Neymotin, Sam [samn at neurosim.downstate.edu];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal cell; Hippocampus CA3 basket cell; GabaA; AMPA; NMDA; Glutamate; I Na,t; I A; I K; I K,leak; I h; I K,Ca; I Sodium; I Potassium; Gaba; Glutamate;
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ca3ihdemo
readme.txt
CA3ih.mod
CA3ika.mod
CA3ikdr.mod
CA3ina.mod
caolmw.mod *
HCN1.mod
icaolmw.mod *
iholmw.mod
ihstatic.mod
kcaolmw.mod *
kdrbwb.mod *
misc.mod *
misc.mod'
misc.mod'A=0
MyExp2SynBB.mod *
MyExp2SynNMDABB.mod *
nafbwb.mod *
stats.mod *
stats.mod'A=0
vecst.mod *
aux_fun.inc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
geom.py
grvec.hoc *
init.hoc
labels.hoc *
local.hoc *
misc.h *
network.py
nqs.hoc *
nrnoc.hoc *
params.py
pyinit.py *
pywrap.hoc
run.py
sim.py
simctrl.hoc *
stats.hoc *
syncode.hoc *
xgetargs.hoc *
                            
TITLE INa CA3

UNITS {
  (mA) = (milliamp)
  (mV) = (millivolt)
}

NEURON {
  SUFFIX nacurrent
  NONSPECIFIC_CURRENT ina
  RANGE g, e, vi, ki
  RANGE minf,hinf,iinf,mtau,htau,itau : testing
}
  
PARAMETER {
  : v	    (mV)
  celsius	    (degC)
  g = 0.032   (mho/cm2)
  e = 55	    (mV)
  vi = -60    (mV)
  ki = 0.8
}
 
STATE {
  m
  h
  I : i 
}
 
ASSIGNED {
  i (mA/cm2)
  ina	(mA/cm2) 
  minf
  mtau    (ms)
  hinf
  htau	(ms)
  iinf
  itau	(ms)
  v	(mV) : testing
}

: PROCEDURE iassign () { ina=g*m*m*m*h*i*(v-e) }
PROCEDURE iassign () { i=g*m*m*m*h*I*(v-e) ina=i}
 
BREAKPOINT {
  SOLVE states METHOD cnexp
  iassign()
}
 
DERIVATIVE states { 
  rates(v)
  m' = (minf - m) / mtau
  h' = (hinf - h) / htau
  : i' = (iinf - i) / itau	    
  I' = (iinf - I) / itau	    
}

INITIAL { 
  rates(v)
  h = hinf
  m = minf
  : i = iinf
  I = iinf
  iassign() : testing
}


PROCEDURE rates(v (mV)) {
  LOCAL  a, b
  UNITSOFF
  a = 0.4*(v+30)/(1-exp(-(v+30)/7.2))
  b = 0.124*(v+30)/(exp((v+30)/7.2)-1) 	
  mtau=0.5/(a+b)
  if (mtau<0.02) {mtau=0.02}
  minf=a/(a+b)
  a = 0.03*(v+45)/(1-exp(-(v+45)/1.5))
  b = 0.01*(v+45)/(exp((v+45)/1.5)-1)
  htau=0.5/(a+b)
  if (htau<0.5) {htau=0.5}
  hinf=1/(1+exp((v+50)/4))
  a =	exp(0.45*(v+66))
  b = exp(0.09*(v+66))
  itau=3000*b/(1+a)
  if (itau<10) {itau=10}
  iinf=(1+ki*exp((v-vi)/2))/(1+exp((v-vi)/2))
  UNITSON
}


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