Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012)

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Accession:147514
" ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. This difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. Our abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity. Alterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex."
Reference:
1 . van der Velden L, van Hooft JA, Chameau P (2012) Altered dendritic complexity affects firing properties of cortical layer 2/3 pyramidal neurons in mice lacking the 5-HT3A receptor. J Neurophysiol 108:1521-8 [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 spiking regular (RS) neuron;
Channel(s): Ca pump;
Gap Junctions:
Receptor(s): 5-HT3;
Gene(s):
Transmitter(s): Serotonin;
Simulation Environment: NEURON;
Model Concept(s): Influence of Dendritic Geometry;
Implementer(s): van der Velden, Luuk [l.j.j.vandervelden at uva.nl];
Search NeuronDB for information about:  5-HT3; Ca pump; Serotonin;
/
dendritic_complexity
README.html
ca.mod *
cad.mod *
cadif.mod
cadif_pump.mod
kca.mod *
km.mod *
kv.mod *
L_HVA_Ca.mod *
na.mod
altered_complexity_model.hoc
mosinit.hoc
screenshot.png
                            
load_file("nrngui.hoc")

/* simulation of neuronal model with varying complexity (set nr 'branch_levels').
A constant apical trunk diameter is used and the rall diameter rule is applied

license: 
author: Luuk van der Velden, University of Amsterdam
paper: Altered dendritic complexity affects firing properties of cortical layer 2/3 pyramidal neurons in mice lacking the 5-HT3A receptor
journal of neurophysiology, 2012

no warranties :)

run as: 'nrngui filename' (easier on the mechanism loads)
*/

// main experimental parameters and parameter initialization

max_branch_levels = 7 //  maximum nr. of branch levels (from 0 to 6)

branch_levels = 1 // nr of branch levels in dendrite (if 0, only apical dendrite is present)

begin_branch_diam = 2.5 // diameter micrometer of apical dendrite
exponent = 3/2 // diameter rule exponent
max_seg_length = 30 // max length of segments (micrometer)


//TotalPump (total density of pump sites on the cell membrane (mol/cm2))
TotalPump_cadp = 0.3e-12

// TotalBuffer (total amount of buffer present)
TotalBuffer_cadp = 0.000

v_init = -70 // initial membrane potential mV (same as rest potential

NDEND = 0 // number of dendrites
NDEND2 = 0 // will be 1 if branch_levels is set to zero 
// in that case an unconnected piece of dendrite is added to the model (treat exceptional case)


// compute the number of dendritic segments (apart from the apical segment)
for i=1, branch_levels {
    NDEND=NDEND+(2^i)
}

NDEND2 = NDEND

// if branch_levels == 0, (NDEND==0) then treat exceptional case (add an unconnected piece of dendrite)
if (NDEND<1){
    NDEND2=1 // one disconnected segment will be created
}

objref leveldiam
leveldiam = new Vector(branch_levels+1,0)

objref levellength
levellength = new Matrix(max_branch_levels+1,max_branch_levels+1)

// Topology
create soma, apical, dend[NDEND2], hillock
access soma

connect soma(0), hillock(1)
connect apical(0), soma(1)

// connect dendritic segments if branch_levels
if (branch_levels>0) {
    for i = 0, branch_levels-1{
        start_dend = 0

        for k = 0, i{
            start_dend=start_dend+2^k
        }

        start_dend=start_dend-1

        for p=0, (2^(i+1))-1{
            if (i<1){
                connect dend[start_dend+p](0),apical(1)

            } else{
                connect dend[start_dend+p](0),dend[(start_dend-(2^i))+int(p/2)](1)
            }
        }
    }
}


// Geometry

path_length_dendr = 250 // path length along dendrite

sc = 0.5 // scaling factor of length between branch levels

// data array of dendritic compartment lengths (branch level dependent)
levellength.x[0][0] = path_length_dendr
levellength.x[1][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[1][1] = path_length_dendr*sc
levellength.x[2][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[2][1] = path_length_dendr*sc - (path_length_dendr*(sc^2))
levellength.x[2][2] = path_length_dendr*(sc^2)
levellength.x[3][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[3][1] = path_length_dendr*sc - (path_length_dendr*(sc^2))
levellength.x[3][2] = (path_length_dendr*(sc^2))-(path_length_dendr*(sc^3))
levellength.x[3][3] = path_length_dendr*(sc^3)
levellength.x[4][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[4][1] = path_length_dendr*sc - (path_length_dendr*(sc^2))
levellength.x[4][2] = (path_length_dendr*(sc^2)) - (path_length_dendr*(sc^3))
levellength.x[4][3] = (path_length_dendr*(sc^3))- (path_length_dendr*(sc^4))
levellength.x[4][4] = (path_length_dendr*(sc^4))
levellength.x[5][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[5][1] = path_length_dendr*sc - (path_length_dendr*(sc^2))
levellength.x[5][2] = (path_length_dendr*(sc^2)) - (path_length_dendr*(sc^3))
levellength.x[5][3] = (path_length_dendr*(sc^3))- (path_length_dendr*(sc^4))
levellength.x[5][4] = (path_length_dendr*(sc^4))- (path_length_dendr*(sc^5))
levellength.x[5][5] = (path_length_dendr*(sc^5))
levellength.x[6][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[6][1] = path_length_dendr*sc - (path_length_dendr*(sc^2))
levellength.x[6][2] = (path_length_dendr*(sc^2)) - (path_length_dendr*(sc^3))
levellength.x[6][3] = (path_length_dendr*(sc^3))- (path_length_dendr*(sc^4))
levellength.x[6][4] = (path_length_dendr*(sc^4))- (path_length_dendr*(sc^5))
levellength.x[6][5] = (path_length_dendr*(sc^5)) - (path_length_dendr*(sc^6))
levellength.x[6][6] = (path_length_dendr*(sc^6))
levellength.x[7][0] = path_length_dendr - (path_length_dendr*sc)
levellength.x[7][1] = path_length_dendr*sc - (path_length_dendr*(sc^2))
levellength.x[7][2] = (path_length_dendr*(sc^2)) - (path_length_dendr*(sc^3))
levellength.x[7][3] = (path_length_dendr*(sc^3))- (path_length_dendr*(sc^4))
levellength.x[7][4] = (path_length_dendr*(sc^4))- (path_length_dendr*(sc^5))
levellength.x[7][5] = (path_length_dendr*(sc^5)) - (path_length_dendr*(sc^6))
levellength.x[7][6] = (path_length_dendr*(sc^6)) - (path_length_dendr*(sc^7))
levellength.x[7][7] = (path_length_dendr*(sc^7))

nr_end_branches = 2^(branch_levels) // number of end points / branches

// compute total length of dendritic tree
total_length = 0 // initial value for sum of total_length
for i = 0, branch_levels{
	total_length = total_length+(levellength.x[branch_levels][i]*(2^i))
}
	
//compute DCI of dendritic trees
DCI = (nr_end_branches+(nr_end_branches*branch_levels))*total_length

print "dci"
print DCI
print "total length"
print total_length

// the Rall value to keep constant
diameter_constant = begin_branch_diam^exponent

// compute diameters for various branch levels (apply Rall rule)
for i=0, branch_levels {
    leveldiam.x[i] = (diameter_constant/(2^i))^(1/exponent)
    print leveldiam.x[i]
}

dend_L = path_length_dendr/(branch_levels+1)

//apical geometry
apical{
    L = levellength.x[branch_levels][0]
    diam = leveldiam.x[0]
    nseg = int(levellength.x[branch_levels][0]/max_seg_length)+1
}

//set dendritic geometry (diameter, length, nr of segments
if (branch_levels>0){

    for i=1, branch_levels{
        start_dend = 0


        for k = 0, i-1 {
        start_dend=start_dend+2^k
        }

        start_dend=start_dend-1

        for p=0, (2^(i))-1{

            dend[start_dend+p]{

                diam = leveldiam.x[i]
                L = levellength.x[branch_levels][i]
                nseg = int(levellength.x[branch_levels][i]/max_seg_length)+1

            }
        }
    }
}

soma_L = 10 // somatic length (micrometer)
soma_diam = 15 // soma diameter (micrometer)

hillock_L = 2 // axon hillock length (micrometer)
hillock_diam = 2 // axon hillock diameter (micrometer)

// set soma geometric properties
soma{
    L = soma_L
    diam = soma_diam
    nseg = 1
}

// set axon hillock geometric properties
hillock{
    L = hillock_L
    diam = hillock_diam
}



// Biophysics
ra        = 150 // axial resistance
global_ra = ra
rm        = 30000  // 30 kilo ohm
c_m       = 0.75 // micro farad

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

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


celsius = 32 // degrees celsius of experiment

Ek = -90 // reversal potential potassium
Ena = 50 // reversal potential sodium


// dendritic conductances 
gna = 1 
gca = 2  
gkca = 10
gkm = 0.1 

// soma conductances
gkv_soma= 800
gna_soma= 300
gca_soma = 0.1
gkca_soma = 0
gkm_soma = 0

//hillock conductances
gkv_hillock = 2000
gna_hillock = 30000


// somatic active channels

soma{
    insert na gbar_na = gna_soma
    insert kv gbar_kv = gkv_soma
    insert ca gbar_ca = gca_soma
    insert cadp
}


// apical active channels
apical{
    insert na	 gbar_na = gna
    insert km    gbar_km  = gkm
    insert ca    gbar_ca = gca
    insert cadp
	insert kca   gbar_kca = gkca

}
  
// axon hilock active channels  
hillock{
	insert na gbar_na = gna_hillock
	insert kv gbar_kv = gkv_hillock
}

// insert passive and active properties in dendritic compartments
for i=0, NDEND-1{
    dend[i]{     
	    insert pas
        Ra = ra
        cm = c_m
        g_pas = 1/rm
        e_pas = v_init
	    insert na	 gbar_na = gna
        insert km    gbar_km  = gkm
        insert ca    gbar_ca = gca
        insert cadp
	    insert kca   gbar_kca = gkca
    }
}

// set general extracellular properties for calcium
forall if(ismembrane("ca_ion")) {
    eca = 140
    ion_style("ca_ion",0,1,0,0,0)
    vshift_ca = 0
}



// Instrumentation

// current input
objref stim
soma stim = new IClamp(0.5)
stim.amp = .02
stim.del = 50
stim.dur = 1000

//Simulation control
tstop = 2000
steps_per_ms = 40
dt = 0.025
nr_cells=int(tstop/dt)+2

// adjust output data size to number of branch levels
if(branch_levels<5){
    nvars = 5 // no thin dendrite calcium dynamics
}else{
    nvars = 6
}
double data[nvars][nr_cells] // create empty array for data storage


// Plotting

// graph of somatic membrane potential
objref g
g = new Graph()
g.size(0,1050,-70,60)
g.addvar("soma.v(0.5)",1,1,0.6,0.9,2)
graphList[1].append(g)
g.save_name("somatic voltage against time")

// Experimental control
proc initialize() {
    t = 0
    cnt = 0
    finitialize(v_init)
    fcurrent()
}

proc integrate() {
    g.begin()
    while (t<tstop) {
        // next section write to the 'data' array
        if(branch_levels<5){
            data[0][cnt]= t
            data[1][cnt] = soma.v(.5)
            data[2][cnt] = soma.cai(.5)
            data[3][cnt] = apical.cai(0.5)
            data[4][cnt] = dend[0].cai(.5) // 1st branch level calcium readout
        }else{
            data[0][cnt]= t
            data[1][cnt] = soma.v(.5)
            data[2][cnt] = soma.cai(.5)
            data[3][cnt] = apical.cai(0.5)
            data[4][cnt] = dend[0].cai(.5)
            data[5][cnt] = dend[14].cai(.5) // 4th branch level calcium readout
        }
        cnt=cnt+1
        g.plot(t)
        fadvance()

    }
    g.flush()
}

// function runs experiment
proc go() {
    initialize()
    integrate()
    finalize()
}

// finalize writes data to file
proc finalize(){

    strdef filename, prefix, postfix
    prefix = "altered_complexity_branch_levels_ratio"
    postfix="txt"
    num = branch_levels
    num2 = exponent
    sprint(filename, "%s_%d_%f.%s", prefix, num, num2,postfix)
    wopen(filename)

    // write data to text file
    for i=0,nr_cells-1{
        if(branch_levels<5){
	        fprint("%f\t %f\t %f\t %f\t %f\n",data[0][i],data[1][i],data[2][i],data[3][i],data[4][i])
        }else{
	        fprint("%f\t %f\t %f\t %f\t %f\t %f\n",data[0][i],data[1][i],data[2][i],data[3][i],data[4][i],data[5][i])
        }
    }
    wopen()
}

go() // run experiment



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