CA1 pyramidal neuron: dendritic Ca2+ inhibition (Muellner et al. 2015)

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Accession:206244
In our experimental study, we combined paired patch-clamp recordings and two-photon Ca2+ imaging to quantify inhibition exerted by individual GABAergic contacts on hippocampal pyramidal cell dendrites. We observed that Ca2+ transients from back-propagating action potentials were significantly reduced during simultaneous activation of individual nearby GABAergic synapses. To simulate dendritic Ca2+ inhibition by individual GABAergic synapses, we employed a multi-compartmental CA1 pyramidal cell model with detailed morphology, voltage-gated channel distributions, and calcium dynamics, based with modifications on the model of Poirazi et al., 2003, modelDB accession # 20212.
Reference:
1 . Müllner FE, Wierenga CJ, Bonhoeffer T (2015) Precision of Inhibition: Dendritic Inhibition by Individual GABAergic Synapses on Hippocampal Pyramidal Cells Is Confined in Space and Time. Neuron 87:576-89 [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: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Calcium; I Sodium; I Potassium; I h;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Dendritic Action Potentials; Active Dendrites; Calcium dynamics;
Implementer(s): Muellner, Fiona E [fiona.muellner at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; I h; I Sodium; I Calcium; I Potassium; Gaba;
function [estimates, model, exitflag, sse] = exp_nooffset2(xdata, ydata,weights)
% Call fminsearch with a random starting point.
% start_point = rand(1, 3);
if nargin<3
    weights = ones(size(ydata));
end
[~,s]=sort(ydata);
f=find(ydata(s)/max(ydata)>0.5,1,'first');
start_point = [rand(1,1)/xdata(s(f))*5];
model = @exp_Yoffs_fun;
options.MaxFunEvals = 10^6*length(start_point);
options.MaxIter = 10^6;
[estimates, sse, fval, exitflag] = fminsearch(model, start_point,options);
% expfun accepts curve parameters as inputs, and outputs sse,
% the sum of squares error for A + B*exp(-lambda*xdata)-ydata,
% and the FittedCurve. FMINSEARCH only needs sse, but we want
% to plot the FittedCurve at the end.
    function [sse, FittedCurve] = exp_Yoffs_fun(params)
        lambda = params(1);
        FittedCurve = exp(-lambda * xdata);
        ErrorVector = FittedCurve - ydata;
        sse = sum(ErrorVector .^ 2.*weights);
    end
end

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