Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:144553
Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. ... Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental determinants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely support higher level cognitive functions including working memory and planning.
Reference:
1 . Amatrudo JM, Weaver CM, Crimins JL, Hof PR, Rosene DL, Luebke JI (2012) Influence of highly distinctive structural properties on the excitability of pyramidal neurons in monkey visual and prefrontal cortices. J Neurosci 32:13644-60 [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: Neocortex; Prefrontal cortex (PFC);
Cell Type(s): Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I N; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Influence of Dendritic Geometry; Detailed Neuronal Models; Electrotonus; Conductance distributions; Vision;
Implementer(s): Weaver, Christina [christina.weaver at fandm.edu];
Search NeuronDB for information about:  Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; I N; I K;
/
V1_PFC_ModelDB
README
kvz_nature.mod *
naz_nature.mod *
vsource.mod *
actionPotentialPlayer.hoc *
add_axon.hoc
analyticFunctions.hoc *
analyze_EPSC.m
aux_procs.hoc
batchrun.hoc
custominit.hoc
define_PFC.hoc
electro_procs.hoc *
figOptions.hoc
fixnseg.hoc *
init_model.hoc
init_PFC.hoc
Jul16IR3f_fromSWCthenManual_Nov22-11.hoc
load_scripts.hoc *
main_fig10_pfc.hoc
main_fig10_v1baseline.hoc
main_fig10_v1tuned.hoc
main_fig9_pfcElec.hoc
main_fig9_v1Elec.hoc
main_PFC-ApBas_fig11epsc.hoc
main_PFC-ApBas_fig12ipsc.hoc
main_V1-ApBas_fig11epsc.hoc
main_V1-ApBas_fig12ipsc.hoc
May3IR2t_ImportFromSWCthenManual_Aug19-11.hoc
measureMeanAtten.hoc
mosinit.hoc
PFC-V1_AddSynapses.hoc
plot_seClamp_i.ses
plot_seClamp_IPSC.ses
read_EPSCsims_mdb.m
read_IPSCsims_mdb.m
readcell.hoc
readNRNbin_Vclamp.m
rigPFCmod.ses
synTweak.hoc
vsrc.ses
                            
/************************************************************

	Christina Weaver
	August 2011
	
	insert channels from Vetter et al (2001) into Jennie's 
	PFC and Visual Cortex neurons.  Includes Mainen's 
	synthetic axon (from the 1995 Mainen et al. paper).

************************************************************/

load_file("nrngui.hoc")

// now load morph
load_file("Jul16IR3f_fromSWCthenManual_Nov22-11.hoc")

load_file("aux_procs.hoc")

xopen("electro_procs.hoc")

    V1_effective_ApicalSpineDensity = 0.67322181	//  total of 1153 spines, divided by total apical length
    V1_effective_BasalSpineDensity  = 0.4886992	//  total of 874 spines, divided by total basal  length

    applySubtreeConstantSpineDensity(apical, SurfaceAreaOneApicalSpine, V1_effective_ApicalSpineDensity)
    applySubtreeConstantSpineDensity(basal,  SurfaceAreaOneBasalSpine,  V1_effective_BasalSpineDensity)

    geom_nseg(100,0.1)

// Using SEClamp, as recommended on the NEURON User Forum.  See init_PFC.hoc for details.
INITDUR = 80	//50

steps_per_ms = 40
dt = 0.025

xopen("PFC-V1_AddSynapses.hoc")

distance()

objref synBranches, synLoc

/*** new parameter settings as of 23 Jan 2012 ***/
VO = -50
V0 = -50	//avoid ambiguity of "Capital O" vs "zero 0"
forall v_init = -50

set_epasNG(72)
scale_gpas(4.8e-5)
scaleNa(25,1e3)
scaleKV(65,16.6667)

forall { if( ismembrane("na") )   vshift_na=-10.5 }  

// soma of V1 cell is smaller than dlPFC soma.
soma.L *= 0.66667
soma.diam *= 0.66667

geom_nseg(100,.1)
distance()

/**** 
    set up a Voltage Clamp
****/

objref seClamp
    soma seClamp = new SEClamp(0.5)
    seClamp.dur1 = 1e9
    seClamp.amp1 = -50	// For IPSCs, neurons are held here to increase driving force.
    seClamp.dur2 = 0

forall { v_init = -50 }


/***** end voltage clamp ****/






/**********************  copied from synTweak.hoc in ~/LuebkeAmatrudo_forCluster  *********/



strdef synFilename
objref synFout, tv, iv


/**********************

    Written originally in main_PFC_simEPSC_all.hoc; now copied here.

    $1  number of synapses
    $2  value of tau1
    $3  value of tau2
    $4  value of gAMPA
    $s5 file basename

**********************/
proc synITweak() { local i, vecsz, btyp

    adjust_Itau1($2,$1)
    adjust_Itau2($3,$1)
    adjust_gGABA($4,$1)

    sprint(synFilename,"%s_INtR%.4f_tF%.2f_gGAB%.5f.Ibin",$s5,$2,$3,$4)
    synFout = new File()
    synFout.wopen(synFilename)

    tv = new Vector()
    tv.record(&t)
    iv = new Vector()
    iv.record(&seClamp.i)

    init()
    run()

    vecsz = tv.size()
    synFout.vwrite(&vecsz)
    tv.fwrite(synFout)
    iv.fwrite(synFout)
    synFout.close()

    sprint(synFilename,"%s_INtR%.4f_tF%.2f_gGAB%.5f_dist.txt",$s5,$2,$3,$4)
    synFout = new File()
    synFout.wopen(synFilename)

    i = 0
    forsec synBranches {
        btyp = 0
	ifsec apical { btyp = 1 }
	ifsec basal  { btyp = 2 }
        synFout.printf("%d\t%g\t%g\t%d\n",i,distance(synLoc.x[i]),distance(synLoc.x[i])-soma.diam,btyp)
        i+=1
    }
    synFout.close()
}



/**********************  end from synTweak.hoc in ~/LuebkeAmatrudo_forCluster  *********/




xopen("plot_seClamp_IPSC.ses")




/*************  now add inhibitory synapses, and activate them one at a time.  ********/

geom_nseg(100,.1)
distance()
nBr = 0

bDen = 0.004
sDen = 0.00142
dDen =   0.00284 
pCut = 156


if( doSynType == 0 ) {

    // activate apical synapses, same gGABA as the dlPFC model
    nSynapse = AddInhSynapses_ProxDist_byDensityAB(bDen,pCut,200,200,synBranches,synLoc,nBr,sDen,dDen, 0)

    endSyn = 200 + (nSynapse + 2)*200
    distance()
    cnt = 0

    tstop=endSyn
    synITweak(nSynapse,2.5,7.5,0.00115,"fig12_V1sameGABA_apic")
}

if( doSynType == 1 ) {

    // activate basal synapses, same gGABA as the dlPFC model
    nSynapse = AddInhSynapses_ProxDist_byDensityAB(bDen,pCut,200,200,synBranches,synLoc,nBr,sDen,dDen, 1)

    endSyn = 200 + (nSynapse + 2)*200
    distance()
    cnt = 0

    tstop=endSyn
    synITweak(nSynapse,2.5,7.5,0.00115,"fig12_V1sameGABA_bas")
}


if( doSynType == 2 ) {

    // activate apical synapses, lower gGABA
    nSynapse = AddInhSynapses_ProxDist_byDensityAB(bDen,pCut,200,200,synBranches,synLoc,nBr,sDen,dDen, 0)

    endSyn = 200 + (nSynapse + 2)*200
    distance()
    cnt = 0

    tstop=endSyn
    synITweak(nSynapse,2.5,7.5,0.00067,"fig12_V1lowGABA_apic")
}

if( doSynType == 3 ) {

    // activate basal synapses, lower gGABA
    nSynapse = AddInhSynapses_ProxDist_byDensityAB(bDen,pCut,200,200,synBranches,synLoc,nBr,sDen,dDen, 1)

    endSyn = 200 + (nSynapse + 2)*200
    distance()
    cnt = 0

    tstop=endSyn
    synITweak(nSynapse,2.5,7.5,0.00067,"fig12_V1lowGABA_bas")
}


{
printf("\n\n**************************\n\n")
printf("Output has been written to a file ending in .Ibin, a customized binary file format.\n")
printf("These files can be read with MATLAB.  See the .m files contained in this directory.\n")
printf("MATLAB's Statistics Toolbox is required to analyze IPSC shapes.\n")
printf("\tSample usage:  read_IPSCsims_mdb('fig12_V1sameGABA_apic',0.00115)\n")
printf("\n\n**************************\n\n")
}

Loading data, please wait...