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

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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;
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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
                            
function [amp, rise, decay, hfwidth, rise10_90, tc_dcy] = analyze_EPSC(t,cur)
%
%   assume we are only sent time vs current data of a single EPSC.

%
%  Find the 'easy' stats: amplitude and half-width.
%
[mn, mnI] = min(cur);
[mx, mxI] = max(cur);
Ishift = cur(1)-cur';
amp = abs(cur(mxI)-cur(mnI));
hfmx = (mn+mx)/2;
hfidx = find(cur<hfmx);
hfbds = [hfidx(1) hfidx(end)];
hfwidth = abs(t(hfbds(2))-t(hfbds(1)));

%
%  now fit exponential.  This is hard because the data are negative and
%  near zero, so linearization doesn't work well. 
%

% Only fit to the part of the curve where the EPSC is 'active':
% assume EPSC starts when when abs(dI/dt) > 1e-5 and ends when abs(dI/dt) <
% 1e-5 for the last time. 
%
[dI]=get_dVdt(t,cur);
[EPidx]=find(abs(dI)>1e-5);
EPst  = EPidx(1);
EPend = EPidx(end);

rise = t(mnI) - t(EPst);
[mn10,idx10] = min(find(abs(Ishift)>=0.1*amp));
[mn90,idx90] = min(find(abs(Ishift)>=0.9*amp));
rise10_90 = t(mn90)-t(mn10);

decay = t(EPend)-t(mnI);
EPbds = [EPst mnI EPend];

% now:  decay time constant as fit to exponential.
tidx = mnI:EPend;
yuse = (find(isfinite(log(Ishift(tidx))) == 1 ));
% paramEstsLin = [ones(size(t(tidx))), t(tidx)] \ log(y(tidx));
% paramEstsLin = [ones(size(t(yuse))), t(yuse)] \ log(Ishift(yuse))';
% paramEstsLin(1) = exp(paramEstsLin(1))
% tc_dcy = 1 / paramEstsLin(2);
% % yoffst = 10;
% % pfit = polyfit(t(yuse),log(Ishift(yuse)+yoffst)',1);
% % tc_dcy = 1 / pfit(1);
% % figure; plot(t,Ishift+10,t(tidx),exp(pfit(2))*exp(t(tidx)*pfit(1)));

% http://www.mathworks.com/products/statistics/demos.html?file=/products/de
% mos/shipping/stats/xform2lineardemo.html
modelFun = @(p,x) p(1)*exp(p(2)*x);

% paramEstsLin = [ones(size(x)), x] \ log(y);
% paramEstsLin(1) = exp(paramEstsLin(1))
% 
% paramEsts = nlinfit(x, y, modelFun, paramEstsLin)

paramEstsLin = [ones(size(t(tidx))) t(tidx)] \ log(Ishift(tidx));
paramEstsLin(1) = exp(paramEstsLin(1));
linFit = myExp(paramEstsLin,t(tidx));

paramEsts = nlinfit(t(tidx), Ishift(tidx), modelFun, paramEstsLin);
iFit = modelFun(paramEsts,t(tidx));
doPlot = 0;
if( doPlot)
    close;
    plot(t,Ishift,'.',t(tidx),iFit,'r-',t(tidx),linFit,'k-');
    legend('data','exp fit','linear fit');
end;

tc_dcy = -1 / paramEsts(2);
% fprintf('');


% fprintf('\tamp %g\n\trise %g VS %g 10-90 pcnt\ndecay %g VS %g\thalf width %g\n',amp,rise,rise10_90,decay,tc_dcy,hfwidth);

if( ~isreal(tc_dcy) )
    tc_dcy = real(tc_dcy);
    fprintf('imaginary!\t');
end;
rise = rise10_90;
decay = tc_dcy;

return;


function [yout] = myExp(p,x)
    yout = p(1).*exp(p(2).*x);
    return;
    
    
function [dVdt] = get_dVdt(tvec, vvec)
% function [dVdt,dVall] = get_dVdt(tvec, vvec)
%
%   get_dVdt    use the central difference formula to estimate the
%               derivative of the second vector with respect to the first. 
%
%   INPUT       TVEC, vector of times
%               VVEC, vector of membrane potential, for example.
%   OUTPUT      dVdt, the derivative of VVEC with respect to TVEC.
%
%   Christina Weaver, christina.weaver@mssm.edu, July 2005
%

for i = 2:length(tvec)-1 
    dVdt(i) = (vvec(i+1)-vvec(i-1))/(tvec(i+1)-tvec(i-1));
end;

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