Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007)

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Accession:95960
Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper.
Reference:
1 . Schmidt-Hieber C, Jonas P, Bischofberger J (2007) Subthreshold dendritic signal processing and coincidence detection in dentate gyrus granule cells. J Neurosci 27:8430-41 [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): Dentate gyrus granule GLU cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; Glutamate;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Parameter Fitting; Influence of Dendritic Geometry; Synaptic Integration;
Implementer(s): Schmidt-Hieber, Christoph [c.schmidt-hieber at ucl.ac.uk];
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; AMPA; Glutamate; Glutamate;
// ----------------------------------------------------------------------------
// membrane.hoc
// loads the full cell morphology, inserts passive
// membrane properties, corrects membrane resistance
// and capacitance for spines, and corrects (roughly) 
// for temperature if needed
//
// 2007-08-17, Christoph Schmidt-Hieber, University of Freiburg
//
// accompanies the publication:
// Schmidt-Hieber C, Jonas P, Bischofberger J (2007)
// Subthreshold Dendritic Signal Processing and Coincidence Detection 
// in Dentate Gyrus Granule Cells. J Neurosci 27:8430-8441
//
// send bug reports and suggestions to christoph.schmidt-hieber@uni-freiburg.de
//
// 2007-08-31: adheres to NetworkReadyCell policy
//
// ----------------------------------------------------------------------------

// load gui or stdrun:
load_file("stdrun.hoc")

load_file("./../share/genutils.hoc")
load_file("./../share/calcSpines.hoc")
load_file("./../share/fixnseg.hoc")

begintemplate cell_10

public is_art, has_bleb, axon_diam0
public init, topol, basic_shape, subsets, geom, biophys, geom_nseg, biophys_inhomo
public synlist, x, y, z, position, connect2target
public somaLoc,somaBorderLoc,blebLoc,distalDendLoc,proxDendLoc,synDendLoc,spineCount,n_sections,n_axon,n_soma
public section, axon
public all,den,axo,som

external verbose,debug_mode,accuracy,calc_spines
external q10_cm,q10_g_pas,q10_Ra,tempScale,geom_nseg_shared,lambda_f

objref somaLoc,somaBorderLoc,blebLoc,distalDendLoc,proxDendLoc,synDendLoc,spineCount,this,synlist

proc init() {
    if (numarg() > 0) {
        has_bleb = $1
    } else {
        has_bleb = 0
    }
    if (numarg() > 1) {
        axon_diam0 = $2
    } else {
        axon_diam0 = 1.2
    }
    topol()
    if (debug_mode) print "Loaded cell, n_sections=",n_sections
    subsets()
    geom()
    biophys()
    geom_nseg()
    synlist = new List()
    synapses()
    x = y = z = 0 // only change via position
}

// dummy compartments, will be updated later:
create axon[1], section[1], soma[1]

proc init_spines() {local i
    forall insert spines
    for i=0,1 section[i] {scale_spines = 1.0}
    for i=2,5 section[i] {scale_spines = 1.5}
    for i=6,13 section[i] {scale_spines = 2.0}
    for i=14,n_sections-1 section[i] {scale_spines = 2.5}
}

func fsigm() {
    // $1: x
    // $2: amplitude
    // $3: center
    // $4: slope
    return $2-$2/(1.0+exp(($1-$3)/$4))
}

proc init_pas() {local Ra_soma, Ra_axon, i, dist
    Ra_soma = 200
    Ra_axon = 120
    forall {
	insert pas
	e_pas=0
	cm = 1.00 * tempScale(q10_cm) * scale_spines
	g_pas = 2.5e-5 * tempScale(q10_g_pas) * scale_spines
	Ra = 200.0 * tempScale(q10_Ra)
    }
    somaLoc.secRef.sec { distance(0,0) }
    for i=0, n_axon-1 axon[i] {
	dist = distance(0.5)
	Ra = (Ra_soma - fsigm(dist, Ra_soma-Ra_axon, 100, 50)) * tempScale(q10_Ra) 
    }
}

proc basic_shape() {local i, soma_rad, soma_lhalf, soma_part0, soma_part1, diam0, diam1, soma_diam0, delta, axon_diam1
    n_sections = 2 + 4 + 8 + 16
    create section[n_sections]
    if (has_bleb == 1) {
        n_axon = 7
    } else {
        n_axon = 9
    }
    create axon[n_axon]
    for i=0, n_axon-1 axon[i] { nseg=5 }
    n_soma = 21
    create soma[n_soma]
    
    soma_rad = 5
    soma_lhalf = 10
    offset = 0.15 // offset to prevent soma from tapering too much
    // elliptic soma:
    for i=0, n_soma-1 soma[i] {
	L = soma_lhalf * 2.0/n_soma
	soma_part0 = (-1.0 + 2.0*(i+offset/2.0)/ (n_soma+offset) ) * soma_lhalf * 0.98
	soma_part1 = (-1.0 + 2.0*(i+1+offset/2.0)/ (n_soma+offset) ) * soma_lhalf * 0.98
	diam0 = sqrt( (1 - soma_part0*soma_part0/ (soma_lhalf*soma_lhalf)) * soma_rad*soma_rad ) * 2.0
	diam1 = sqrt( (1 - soma_part1*soma_part1/ (soma_lhalf*soma_lhalf)) * soma_rad*soma_rad ) * 2.0
	diam(0:1) = diam0:diam1
	if (i==0) {
            soma_diam0 = diam0
        }
    }
    
    axon[0] { diam(0:1)=soma_diam0:axon_diam0 L = 8 }
    
    // linear taper:
    axon_diam1 = 0.3
    delta = (axon_diam0-axon_diam1) / 20.0
    for i=1,5 axon[i] {
        L = 4 
        diam(0:1)=axon_diam0-(i-1)*delta*L : axon_diam0-i*delta*L
    }

    if (has_bleb == 1) {
	axon[6] { 
            diam = 2.0
            L = 2.0
            blebLoc = new Location(0.0)
        }
	
    } else {
	axon[6] { diam = axon_diam1 L = 500 }
	axon[7] { 
            diam = 3.0 // 0.4
            L = 3.0 // 3.0
            blebLoc = new Location(0.5)    
        }
	axon[8] { diam = axon_diam1 L = 470 } 
    }

    for i=0,1 section[i]  { diam(0:1)=soma[n_soma-1].diam(1.0):2.0 L=20 }
    for i=2,5 section[i]  { diam(0:1)=2.0:1.5 L=80 }
    for i=6,13 section[i] { diam = 0.9 L=100 }
    for i=14,n_sections-1 section[i] { diam=0.6 L=100 }
    
    // define soma:
    soma[n_soma/2.0]  somaLoc = new Location(0.5)
    soma[0] somaBorderLoc = new Location(0.0)
    // define dendritic sites:
    section[n_sections-1] distalDendLoc = new Location(0.8)
    section[1] proxDendLoc = new Location(0.05)
    section[n_sections-5] synDendLoc = new Location(0.8)
    
    access somaLoc.secRef.sec
}

proc topol() {local i
    basic_shape()
    for i=0,1 section[i] {  connect section[i](0.0), soma[n_soma-1](1.0) }
    for i=2,n_sections-1 section[i] { connect section[i](0.0), section[int((i-2)/2.0)](1.0) }
    
    for i=1, n_soma-1 soma[i] { connect soma[i](0.0), soma[i-1](1.0) }
    
    connect axon[0](0.0), soma[0](0.0)

    for i=1, n_axon-1 axon[i] { connect axon[i](0.0), axon[i-1](1.0) }

    init_spines()
}

objref all,den,axo,som
proc subsets() {local i
    all = new SectionList()
    for i=0, n_sections-1 section[i] all.append()
    for i=0, n_axon-1 axon[i] all.append()
    for i=0, n_soma-1 soma[i] all.append()
    den = new SectionList()
    for i=0, n_sections-1 section[i] den.append()
    axo = new SectionList()
    for i=0, n_axon-1 axon[i] axo.append()
    som = new SectionList()
    for i=0, n_soma-1 soma[i] som.append()
}

proc geom() {
}

proc geom_nseg() {
    geom_nseg_shared()
    // increase nseg even further (tribute to Josef):
    if (accuracy >= 1) {
	forall nseg*=3
    }
}

proc biophys() {
    init_pas()
}

proc biophys_inhomo(){}

proc position() { local i
    somaLoc.secRef.sec for i = 0, n3d()-1 {
	pt3dchange(i, $1-x+x3d(i), $2-y+y3d(i), $3-z+z3d(i), diam3d(i))
    }
    x = $1  y = $2  z = $3
}

obfunc connect2target() {localobj nc //$o1 target point process, optional $o2 returned NetCon
    axon[0] nc = new NetCon(&v(0), $o1)
    nc.threshold = 10
    if (numarg() == 2) { $o2 = nc } // for backward compatibility
    return nc
}

objref syn_
proc synapses() {
}

func is_art() { return 0 }

endtemplate cell_10

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