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: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 GLU cell; Hippocampus CA3 interneuron basket GABA 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 [Samuel.Neymotin at nki.rfmh.org];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA 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 *
MyExp2SynBB.mod *
MyExp2SynNMDABB.mod *
nafbwb.mod *
stats.mod *
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 *
                            
import sys
import os
import string
from neuron import *
h("strdef simname, allfiles, simfiles, output_file, datestr, uname, osname, comment")
h.simname=simname = "mtlhpc"
h.allfiles=allfiles = "geom.hoc pyinit.py geom.py network.py params.py run.py"
h.simfiles=simfiles = "pyinit.py geom.py network.py params.py run.py"
h("runnum=1")
runnum = 1.0
h.datestr=datestr = "10dec15"
h.output_file=output_file = "data/10dec13.14"
h.uname=uname = "x86_64"
h.osname=osname="linux"
h("templates_loaded=0")
templates_loaded=0
h("xwindows=1.0")
xwindows = 1.0
h.xopen("nrnoc.hoc")
h.xopen("init.hoc")
from pyinit import *
execfile("geom.py")
execfile("network.py")
execfile("params.py")
execfile("run.py") 
from pylab import * # need matplotlib 

h.tstop = 1e3
if len(sys.argv) > 3:
  h.tstop = float(sys.argv[3])
  print 'set h.tstop to',h.tstop

# scaleihpyr - scale pyramidal cell ih conductance
def scaleihpyr (net,fctr):
  for cell in net.pyr.cell:
    for sec in cell.all_sec:
      sec(0.5).hcurrent.gfactor = fctr

# scaleihbas - scale basket cell ih conductance
def scaleihbas (net,fctr):
  for cell in net.bas.cell:
    cell.soma(0.5).HCN1.gfactor = fctr

# scaleiholm - scale olm cell ih conductance
def scaleiholm (net,fctr):
  for cell in net.olm.cell:
    cell.soma(0.5).Iholmw.gfactor = fctr

#  run sim varying pyr and bas ih
def runihbaspyr ():
  llfp = []
  for ih in [0.0, 0.5, 1.0, 1.5, 2.0]:
    print 'setting pyr,bas Ih to', ih , 'X baseline'
    scaleihbas(net,ih)
    scaleihpyr(net,ih)
    h.run()
    net.calc_lfp()
    llfp.append( net.vlfp.to_python() )
  return llfp

llfp = runihbaspyr()

# draw the lfps in order of increasing Ih (bottom to top)
def drawlfps (xl=[-1,-1],killms=200):
  tt = linspace(0,h.tstop/1e3,len(llfp[0]))
  clrs = ['k','r','b','g','DarkOrange']
  sidx,eidx = int(killms/h.dt),int((h.tstop-killms)/h.dt)
  starty = amin(llfp[0])
  for i in xrange(5):
    na = numpy.array(llfp[i])+starty
    plot(tt[sidx:eidx],na[sidx:eidx],clrs[i])
    starty += amax(llfp[i])
  xlabel('Time (s)');
  if xl[0] > -1 and xl[1] > -1:  xlim(xl)
  tight_layout()

# get a list of PSDs for the different Ih values
def getlpsd (killms=200):
  lpsd = []
  for lfp in llfp:
    na = numpy.array(lfp)
    vec=h.Vector()
    sidx,eidx = int(killms/h.dt),int((h.tstop-killms)/h.dt)  
    na = na[sidx:eidx]
    na -= mean(na);
    win = numpy.kaiser(len(na),5);
    na = na * win;
    vec.from_python(na)
    lpsd.append(h.nrnpsd(vec,1e3/h.dt))
    na = numpy.array(lpsd[-1].v[1].to_python())
    win = numpy.kaiser(25,5);
    na = numpy.convolve(na,win,'same');
    lpsd[-1].v[1].from_python(na)        
  return lpsd

lpsd = []
if h.tstop >= 7.99e3: lpsd = getlpsd()

# plot the PSD
def plotpsd ():
  clrs = ['k','r','b','g','DarkOrange']
  for i in xrange(5):
    plot(lpsd[i].v[0].to_python(),lpsd[i].v[1].to_python(),clrs[i]);
    xlim((0,50));

# get the peaks. rns specifies min,max frequencies.
def getpks (rng):
  lx,ly=[],[]
  for psd in lpsd:
    psd.verbose=0
    psd.select('f','[]',rng[0],rng[1])
    pkx,pky = psd.getcol('f').x[int(psd.getcol('pow').max_ind())],psd.getcol('pow').max()
    lx.append(pkx); ly.append(pky)
    psd.verbose=1
  return lx,ly

# plot the peaks
def plotpks (lx,ly,band):
  clrs = ['k','r','b','g','DarkOrange']
  scatter(lx,ly,s=100,c=clrs)
  xlabel(band + ' peak (Hz)')
  ylabel(band + ' Power')
  tight_layout()

# draw the output
def drawfig ():
  if h.tstop >= 7.99e3:
    subplot(3,1,1); drawlfps(xl=[2,3]); 
    ltx,lty=getpks([4,12]); subplot(3,1,2); plotpks(ltx,lty,'Theta');
    lgx,lgy=getpks([25,55]); subplot(3,1,3); plotpks(lgx,lgy,'Gamma');
  else:
    drawlfps()
  show()

drawfig()


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