L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:150538
This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model.
Reference:
1 . Li X, Morita K, Robinson HP, Small M (2013) Control of layer 5 pyramidal cell spiking by oscillatory inhibition in the distal apical dendrites: a computational modeling study. J Neurophysiol 109:2739-56 [PubMed]
Citations  Citation Browser
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: Neocortex;
Cell Type(s):
Channel(s): I K,Ca; I Na, leak;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Dopamine;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Intrinsic plasticity;
Implementer(s): Moradi, Keivan [k.moradi at gmail.com]; Robinson, H.P.C. [hpcr at cam.ac.uk]; Small, Michael ; Li, Xiumin ;
Search NeuronDB for information about:  AMPA; I K,Ca; I Na, leak; Dopamine;
/
XiEtal2013
Codes for periodic inhibition
ReadMe.txt
cad2.mod
GABABsyn.mod
h.mod
kca.mod *
km.mod *
kv.mod *
na.mod *
NMDAr.mod
SlowCa.mod *
basal_soma_periodicgaba_stimulus.hoc
basal_soma_Poissongaba_stimulus.hoc
distal_distributed_periodic_gaba_stimulus.hoc
distal_distributed_periodic_gaba+gabab_stimulus.hoc
distal_distributed_Poisson_gaba_stimulus.hoc
distal_distributed_Poisson_GABAb_stimulus.hoc
j4a.hoc *
mainfile_stim_cyc.hoc
mosinit.hoc
                            
/* --------------------------------------------------------------
   mainfile for oscillatory inhibition onto the layer V pyramidal neuron



   The Neuron Model code is adapted from Mainen Nature 1996.
   Parameters are based on "Schaefer, Larkum, Sakmann, Roth
   "Coincidence detection in pyramidal neurons is tuned by their
   dendritic branching pattern" J. Neurophysiol. 89:3134-3154, 2003
 
   -------------------------------------------------------------- */

load_file("nrngui.hoc")

objref axonal, dendritic, dendritic_only, distaldend, basaldend

create soma,iseg,hill,myelin[2],node[2], dend11[1]

// --------------------------------------------------------------
// passive & active membrane 
// --------------------------------------------------------------

ra        = 150			
global_ra = ra
rm        = 30000			
c_m       = 0.75
cm_myelin = 0.04
g_pas_node = 0.02

v_init    = -70            
celsius   = 34			

Ek = -90
Ena = 60

gna_dend =50                           //(pS/um2)
gna_node =30000                       
gna_soma =54                         
gna_myelin =80                    

gkv_axon = 2000
gkv_soma = 300 
gkv_dend = 1


gca_soma = 0
gca_dend =0.1
gca_proximal=3

ghbar=0.001      //mho/cm2  
zetat=2.2        //time scale of activation of h current
tau=1 

gkm_dend =.1
gkm_soma = 0.2 

gkca_soma = 6.5 
gkca_dend=3.25 

// --------------------------------------------------------------
// Axon geometry
//
// Similar to Mainen et al (Neuron, 1995)
// --------------------------------------------------------------

n_axon_seg = 5

proc create_axon() {

  create iseg,hill,myelin[n_axon_seg],node[n_axon_seg]

  soma {
    equiv_diam = sqrt(area(.5)/(4*PI))

    // area = equiv_diam^2*4*PI
  }
  if (numarg()) equiv_diam = $1

  iseg {                // initial segment between hillock + myelin
     L = 15
     nseg = 5
     diam = equiv_diam/10        // see Sloper and Powell 1982, Fig.71
  }

  hill {                
    L = 10
    nseg = 5
    diam(0:1) = 4*iseg.diam:iseg.diam
  }

  // construct myelinated axon with nodes of ranvier

  for i=0,n_axon_seg-1 {
    myelin[i] {         // myelin element
      nseg = 5
      L = 100
      diam = iseg.diam         
    }
    node[i] {           // nodes of Ranvier
      nseg = 1
      L = 1.0           
      diam = iseg.diam*.75       // nodes are thinner than axon
    }
  }

  soma connect hill(0), 0.5
  hill connect iseg(0), 1
  iseg connect myelin[0](0), 1
  myelin[0] connect node[0](0), 1

  for i=0,n_axon_seg-2  { 
      node[i] connect myelin[i+1](0), 1 
      myelin[i+1] connect node[i+1](0), 1
  }
}

// --------------------------------------------------------------
// Spines
// --------------------------------------------------------------

      // Based on the "Folding factor" described in
      // Jack et al (1989), Major et al (1994)
      // note, this assumes active channels are present in spines 
      // at same density as dendrites

spine_dens = 1
      // just using a simple spine density model due to lack of data on some 
      // neuron types.

spine_area = 0.83 // um^2  -- K Harris

proc add_spines() { local a
  forsec $o1 {
    a =0
    for(x) a=a+area(x)

    F = (L*spine_area*spine_dens + a)/a

    L = L * F^(2/3)
    for(x) diam(x) = diam(x) * F^(1/3)
  }
}

// --------------------------------------------------------------
// Initialization routines
// --------------------------------------------------------------

proc init_cell() {	


  // passive
  forall {
    insert pas
    Ra = ra 
    cm = c_m 
    g_pas = 1/rm
    e_pas = v_init
  }

  d=dend11[70].diam                 // exchange the original diam of dend11[70] and dend11[58] to make it reseanable
  dend11[70].diam=dend11[58].diam
  dend11[58].diam=d

  // exceptions along the axon
  forsec "myelin" cm = cm_myelin
  forsec "node" g_pas = g_pas_node

  // active 
  // axon
  forall insert na      
  forsec "myelin" gbar_na = gna_myelin
  forsec "hill" gbar_na = gna_node
  forsec "iseg" gbar_na = gna_node
  forsec "node" gbar_na = gna_node
  forsec "iseg" { insert kv  gbar_kv = gkv_axon }
  forsec "hill" { insert kv  gbar_kv = gkv_axon }

  // dendrites
  forsec dendritic_only {
                 gbar_na = gna_dend
    insert kv    gbar_kv = gkv_dend 
    insert km    gbar_km  = gkm_dend
    insert kca   gbar_kca = gkca_dend
    insert sca    gbar_sca = gca_dend
    insert cad2  //calcium concentration
    cao = 2.5  //mM
  }

  // soma
  soma {
                 gbar_na = gna_soma
    insert kv    gbar_kv = gkv_soma 
    insert km    gbar_km = gkm_soma
    insert kca   gbar_kca = gkca_soma 
    insert sca   gbar_sca = gca_soma
    insert cad2                                   
    cao = 2.5                //mM
  }

for i=22,82 {
    access dend11[i]
    insert hd
    ghdbar_hd=ghbar
    zetat_hd=zetat     ////time scale of activation of h current
    tau_hd=tau                                                         
}


//////////////////////////////////calcium for hot zone////////////////////////////////////////////
dend11[23].gbar_sca = gca_proximal
dend11[24].gbar_sca = gca_proximal
dend11[39].gbar_sca= gca_proximal

access dend11[58]
        for j = 1, nseg/2 {
           node_pos = (2*j-1)/(2*nseg)
           gbar_sca(node_pos)=gca_proximal
        }

// --------------------------------------------------------------
// seems to be necessary for 3d cells to shift Na kinetics -5 mV
// --------------------------------------------------------------

  forall if(ismembrane("k_ion")) ek = Ek
  forall if(ismembrane("na_ion")) {
    ena = Ena
    vshift_na = -5
  }

    forall if(ismembrane("ca_ion")) {
    //eca = 140
    ion_style("ca_ion",0,1,0,0,0)
    vshift_ca = -10
  } 

}

// --------------------------------------------------------------
// loading cell
// --------------------------------------------------------------

proc load_3dcell() {
  // $s1 filename

  aspiny = 0
  forall delete_section()
  xopen($s1)
  access soma
  forsec "axon" delete_section()
  dendritic = new SectionList()

  // make sure no compartments exceed 20 uM length

  forall {
    if(nseg < L/20) { print secname(), " not accurate" nseg=L/20+1 }
    dendritic.append()
  }    

  
  dendritic_only = new SectionList()
  forsec dendritic dendritic_only.append()
  soma dendritic_only.remove()

  distaldend = new SectionList()
  basaldend = new SectionList()
       
  for i=23,82 {
       access dend11[i]
       if (i!=23 && i!=24 && i!=39 && i!=58) {
          distaldend.append()
       }
  }

  forsec dendritic_only basaldend.append()
  forsec distaldend basaldend.remove()
  dend11[22] basaldend.remove()
  dend11[23] basaldend.remove()
  dend11[24] basaldend.remove()
  dend11[39] basaldend.remove()
  dend11[58] basaldend.remove()

  create_axon()  
  if (!aspiny) add_spines(dendritic_only,spine_dens)
}

// --------------------------------------------------------------
// Main Loading procedure
// --------------------------------------------------------------

proc LoadNInit() {
  load_3dcell("j4a.hoc")
  init_cell()
}
LoadNInit()

proc initialize()  {
     finitialize(v_init)
     fcurrent()
} 

//////////////////////////////////////////////////////////////////////////////////////////
/*simulation control*/
//////////////////////////////////////////////////////////////////////////////////////////

tstop = 11000  //ms
dt = 0.05      //ms
nrnmainmenu()
{
steps_per_ms = 20
xpanel("RunControl", 0)
xbutton("Init, Run & Save","circ_run()")
xbutton("Stop","stoprun=1")
xbutton("Single Step","steprun()")
t = 0
xvalue("t","t", 2 )
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )

xvalue("dt","dt", 1,"setdt()", 0, 1 )

xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(701,27)
}


//////////////////////////////////////////////////////////////////////////////////////////////////////
/*plot*/
//////////////////////////////////////////////////////////////////////////////////////////////////////

objref newgraphv
newgraphv = new Graph(0)
addplot(newgraphv,0)
newgraphv.size (0,tstop+100,-100, 50)
newgraphv.view(0, -100, tstop+100, 150, 6, 0, 500, 200)  //.view(mleft, mbottom, mwidth, mheight, wleft,wtop, wwidth, wheight)

newgraphv.addvar("soma.v(0.5)", 1, 2, 0.6, 0.9, 2) //colour, line width,... 
newgraphv.addvar("dend11[26].v(0.5)", 3, 2, 0.6, 0.9, 2)
//newgraphv.label("Distributed Stimulus")

/*
objref newgraphc
newgraphc = new Graph(0)
addplot(newgraphc,0)
newgraphc.size (0,tstop+100,-100, 50)
newgraphc.view(0, -0.003, tstop+100, 0.005, 6, 250, 500, 200) 
//newgraphc.addvar("dend11[26].ica(0.5)", 2, 2, 0.6, 0.9, 2)
newgraphc.addvar("dend11[26].i_hd(0.5)", 1, 2, 0.6, 0.9, 2)
//newgraphc.addvar("dend11[26].ina(0.5)", 2, 1, 0.6, 0.9, 2)
*/

/*
objectvar save_window_
objectvar scene_vector
{
save_window_ = new Graph(0)
save_window_.size(0,tstop+100,0,0.017)
scene_vector = save_window_
{save_window_.view(0, 0.3, tstop+100, 0.4, 6, 600, 500, 200)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
//save_window_.addexpr("AMPAsyn[0].g", 1, 1, 0.8, 0.9, 2)		/////later change this
//save_window_.addexpr("NMDAsyn[0].g", 7, 1, 0.8, 0.9, 2)
//save_window_.addexpr("GABAsyn[0].g", 3, 1, 0.8, 0.9, 2)
save_window_.addexpr("GABAbsyn[0].g", 2, 1, 0.8, 0.9, 2)
}
*/
/////////////////////////////////////////////////////////////////////
/*initialize, run and save soma.v(.5)*/
/////////////////////////////////////////////////////////////////////

N_parameter=1  //number of loops for different sets of parameters
N_frequency=21 //number of loops for different frequencies of oscillaotory input 

objref recv[N_frequency]
objref savdata_somav
objref tempmatrix


strdef basename1, extension1, filename1           

objectvar savdata_somav
basename1 = "Outputdata/somav_disperi_delta_"
extension1 = "dat"


proc savedata()   {


for i=0,N_frequency-1 {
    recv[i]=new Vector()
    recv[i].record(&soma.v(0.5))
    //recv[i].record(&dend11[26].v(0.5))

}	
   
    initialize()
    run()	
    sprint(filename1, "%s%g.%s", basename1,delta,extension1)
    savdata_somav = new File(filename1)
    savdata_somav.wopen()                       //comment this line if not saving the data

    tempmatrix.setrow(index,recv[index])
    tempmatrix.fprint(0,savdata_somav, " %g")
    savdata_somav.close()
}


///////////////////////////////////////////////////////////////////////////////////////////////////
proc circ_run() {

 for nn=1,N_parameter {

    tempmatrix = new Matrix()
    tempmatrix.resize(N_frequency, tstop/dt+1)
   
    index=0
    events=2000      //total events of GABAa synapses per second, 1000+(nn-1)*500
    alfa=1           //scaling parameter for changing the time constant of GABAa synapses, 1.5+0.5*(nn-1)
    amp1=0.5         //tau1 of the GABAa synapses
    amp2=alfa*7      //tau2 of the GABAa synapses
    delta=5          //value of sigma^2 of Gaussian distribution for periodic inhibitory inputs

    nsyn_start_GLUT=200         //number of distal GLUT synapses

    for mm=1,N_frequency {

        //basal inputs

        gamma1=0.025        //nS single channel conductance of NMDA synapses         
        AMPA_weight1=100e-6 //uS
        GABA_weight1=0      //uS

        //distal inputs

        gamma=0.025         //nS  single channel conductance of NMDA synapses        
        AMPA_weight=100e-6  //uS
        GABA_weight=1000e-6 //uS 
        GABAb_weight=0      //uS


        StimStart=0

        nsyn_start_GLUT1=100        //number of basal GLUT synapses    

        GABAprefreq_per1=40         //frequency of each somatic GABAa synapse

        xopen("basal_soma_Poissongaba_stimulus.hoc")                    //basal Poisson excitation + somatic Poisson inhibition
        //xopen("basal_soma_periodicgaba_stimulus.hoc")                 //basal Poisson excitation + somatic periodic inhibition
                    

        GABAprefreq_per=2+(mm-1)*4                                      //frequency of each distal GABAa synapse (2 to 82 Hz)

        xopen("distal_distributed_periodic_gaba_stimulus.hoc")          //distal periodic GABAa input
                 
        //xopen("distal_distributed_Poisson_gaba_stimulus.hoc")         //distal Possion GABAa input   

        //xopen("distal_distributed_periodic_gaba+gabab_stimulus.hoc")  //distal periodic GABAa + GABAb input  
                 
        //xopen("distal_distributed_Poisson_GABAb_stimulus.hoc")        //distal Possion GABAb input


        savedata()
        index=index+1
     }
  }
}

proc demo_run() {
  tstop_orig=tstop 
  N_frequency_orig = N_frequency
  tstop = 250 
  N_frequency = 1

  circ_run()

  tstop=tstop_orig
  Graph[0].exec_menu("View = plot")
  N_frequency=N_frequency_orig
}

xpanel("Li et al. 2013 run choices")
  xbutton("Short demo run", "demo_run()")
  xbutton("Circular run","circ_run()")
xpanel()