Simulations of modulation of HCN channels in L5PCs (Mäki-Marttunen and Mäki-Marttunen, 2022)

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Accession:267293
"... In this work, we build upon existing biophysically detailed models of thick-tufted layer V pyramidal cells and model the effects of over- and under-expression of Ih channels as well as their neuromodulation by dopamine (gain of Ih function) and acetylcholine (loss of Ih function). We show that Ih channels facilitate the action potentials of layer V pyramidal cells in response to proximal dendritic stimulus while they hinder the action potentials in response to distal dendritic stimulus at the apical dendrite. We also show that the inhibitory action of the Ih channels in layer V pyramidal cells is due to the interactions between Ih channels and a hot zone of low voltage-activated Ca2+ channels at the apical dendrite. Our simulations suggest that a combination of Ih-enhancing neuromodulation at the proximal apical dendrite and Ih-inhibiting modulation at the distal apical dendrite can increase the layer V pyramidal excitability more than any of the two neuromodulators alone..."
Reference:
1 . Mäki-Marttunen T, Mäki-Marttunen V (2022) Excitatory and inhibitory effects of HCN channel modulation on excitability of layer V pyramidal cells Plos Comp Biol [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex layer 5 pyramidal cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Acetylcholine; Dopamine; Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Neuromodulation;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  AMPA; NMDA; Gaba; Acetylcholine; Dopamine; Gaba; Glutamate;
#cp ../haymod3e/runcontrol_check.py runcontrol.py
# runcontrols
# A script for determining the control neuron F-I curve and limit cycle.
#
# The input code for the hoc-interface is based on BAC_firing.hoc by Etay Hay (2011)
#
# Tuomo Maki-Marttunen, Oct 2014
# (CC BY)

from neuron import h
import mytools
import pickle
import numpy as np
import sys

spikfreqsAll = []
timescAll = []
VsomacAll = []
VDerivcAll = []
VDcoeffAll = []
VdendcAll = []
VdDcoeffAll = []
VdDerivcAll = []
CasomacAll = []
CaDerivcAll = []
CaDcoeffAll = []
CadendcAll = []
CadDerivcAll = []
CadDcoeffAll = []
times_controlAll = []
Vsoma_controlAll = []
Vdend_controlAll = []
Casoma_controlAll = []
Cadend_controlAll = []

Ihmod = 0.0
if len(sys.argv) > 1:
  Ihmod = float(sys.argv[1])

for icell in range(0,1):
  morphology_file = "morphologies/cell"+str(icell+1)+".asc"
  biophys_file = "models/L5PCbiophys3.hoc"
  template_file = "models/L5PCtemplate.hoc"
  v0 = -80
  ca0 = 0.0001

  proximalpoint = 400
  distalpoint = 620
  BACdt = 5.0

  h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
cvode.atol(0.0002)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
access L5PC.soma
objref st1
st1 = new IClamp(0.5)
L5PC.soma st1

objref vsoma, vdend, recSite, vdend2, isoma, cadend, cadend2, casoma
vsoma = new Vector()
casoma = new Vector()
vdend = new Vector()
cadend = new Vector()
vdend2 = new Vector()
cadend2 = new Vector()
objref sl,ns,tvec
tvec = new Vector()
sl = new List()
double siteVec[2]
sl = L5PC.locateSites("apic","""+str(distalpoint)+""")
maxdiam = 0
for(i=0;i<sl.count();i+=1){
  dd1 = sl.o[i].x[1]
  dd = L5PC.apic[sl.o[i].x[0]].diam(dd1)
  if (dd > maxdiam) {
    j = i
    maxdiam = dd
  }
}
siteVec[0] = sl.o[j].x[0]
siteVec[1] = sl.o[j].x[1]
print "distalpoint gCa_HVA: ", L5PC.apic[siteVec[0]].gCa_HVAbar_Ca_HVA
print "distalpoint gCa_LVA: ", L5PC.apic[siteVec[0]].gCa_LVAstbar_Ca_LVAst
L5PC.apic[siteVec[0]] cvode.record(&v(siteVec[1]),vdend,tvec)
L5PC.apic[siteVec[0]] cvode.record(&cai(siteVec[1]),cadend,tvec)
L5PC.soma cvode.record(&v(0.5),vsoma,tvec)
L5PC.soma cvode.record(&cai(0.5),casoma,tvec)
""")

  #Block or amplify Ih channels:
  h("""forall if(ismembrane("Ih")) { offma_Ih = offma_Ih + """+str(Ihmod)+""" } """)

  Is = [0.1*x for x in range(0,16)]
  spikfreqs = len(Is)*[0]
  Is_this = [0.0,1.0, 0.5]
  Niter = 15
  Vmaxs_tested = []
  Vmaxdends_tested = []
  Is_tested = []
  for iI in range(0,Niter):
    squareAmp = Is_this[min(iI,2)]
    squareDur = 3800
    tstop = 14000
    h("""
tstop = """+str(tstop)+"""
v_init = """+str(v0)+"""
cai0_ca_ion = """+str(ca0)+"""
st1.amp = """+str(squareAmp)+"""
st1.dur = """+str(squareDur)+"""
st1.del = 10200
""")
    h.init()
    h.run()

    times=np.array(h.tvec)
    Vsoma=np.array(h.vsoma)
    Vdend=np.array(h.vdend)
    Casoma=np.array(h.casoma)
    Cadend=np.array(h.cadend)
    spikes = mytools.spike_times(times,Vsoma,-35,100)
    if iI < 2:
      continue
    if len(spikes) > 0:
      Is_this = [Is_this[0], Is_this[2], (Is_this[0]+Is_this[2])/2]
    else:
      Is_this = [Is_this[2], Is_this[1], (Is_this[2]+Is_this[1])/2]

    Vmaxs_tested.append(max(Vsoma))
    Vmaxdends_tested.append(max(Vdend))
    Is_tested.append(Is_this[min(iI,2)])

    
picklelist = [Is_tested,Vmaxs_tested,Vmaxdends_tested]
file = open('fI_wait_Ihmod'+str(Ihmod)+'_thresh.sav', 'wb')
pickle.dump(picklelist,file)
file.close()



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