Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)

 Download zip file 
Help downloading and running models
The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
1 . Yim MY, Hanuschkin A, Wolfart J (2015) Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability. Hippocampus 25:297-308 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Dentate gyrus;
Cell Type(s): Dentate gyrus granule GLU cell; Dentate gyrus mossy cell; Dentate gyrus basket cell; Dentate gyrus hilar cell; Dentate gyrus MOPP cell;
Channel(s): I Chloride; I K,leak; I Cl, leak; Kir; Kir2 leak;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s): IRK; Kir2.1 KCNJ2; Kir2.2 KCNJ12; Kir2.3 KCNJ4; Kir2.4 KCNJ14;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Spatio-temporal Activity Patterns; Intrinsic plasticity; Pathophysiology; Epilepsy; Homeostasis; Pattern Separation;
Implementer(s): Yim, Man Yi [manyi.yim at]; Hanuschkin, Alexander ; Wolfart, Jakob ;
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; I Chloride; I K,leak; I Cl, leak; Kir; Kir2 leak; Gaba; Glutamate;
%%% Analysis of DG network data %%%
% This Matlab code fits the data using least-squares methods:
% "Least squares" means that the overall solution minimizes the sum of the squares of the errors made in the results of every single data point
% Enter the files to import.

% ModelDB file along with publication:
% Yim MY, Hanuschkin A, Wolfart J (2015) Hippocampus 25:297-308.
% modified and augmented by
% Man Yi Yim / 2015
% Alexander Hanuschkin / 2011

close all;

offset_corrected = 4;   % 0= not corrected; 1= corrected <AH> 2= corrected <MY> 3= two parameters (formula 1) 4= two parameters (formula 2) 5= three parameters
simscore1_corrected = 0;
fig1 = figure(1);

idname = '-pp10-gaba1-kir1-st0';
for figure_nr = 1:1
        A = importdata(strcat('sim_score',idname,'.txt'));

        % power law fit (
        fun = @(x,xdata)xdata.^x(1);    % function to fit
        x0 = .1;                        % initial values for fitting parameters
        xdata = A(:,1);                 % x values data
        ydata = A(:,2);                 % y values data
        switch offset_corrected
            case 1
                ydata = ydata - (mean(ydata(find(xdata<0))));
                if (simscore1_corrected==1)
                    ydata = ydata * 1/ydata(find(A(:,1)==1));
            case 2
                % power law fit (
                % offset  
                offset = mean(ydata(find(xdata<0)));
                fun = @(x,xdata)offset+(1-offset)*xdata.^x(1);    % function to fit
            case 3
                fun = @(x,xdata)x(1)*(1-xdata)+xdata.^x(2);
                x0 = [0.1, 3];
            case 4
                fun = @(x,xdata)x(1) + (1-x(1))*xdata.^x(2);
                x0 = [0.1, 1];
            case 5
                %offset = mean(ydata(find(xdata<0)));
                fun = @(x,xdata)x(1)*(1-xdata).^x(2)+xdata.^x(3);
                x0 = [0.1, 1, 3];
        x =  lsqcurvefit(fun,x0,xdata,ydata);   %
        line([0 1],[0 1],'LineStyle','--','Color','blue','LineWidth',3,'MarkerSize',1); hold on;
        line([0.6 0.6],[0 1],'LineStyle','-.','Color','green','LineWidth',3,'MarkerSize',1); hold on;
        h1 = plot([0:0.005:1],fun(real(x),[0:0.005:1]),'-','Color','red','LineWidth',3,'MarkerSize',1);hold on;
        ts = sprintf('fit x^{%.3f}',x);
        h2 = plot(xdata,ydata,'o','Color',[1 0.50 0.25],'LineWidth',2,'MarkerSize',10);hold on;
        xlim([-0.05 1.05]);
        ylim([-0.05 1.05]);
        title_str = sprintf('Similarity Score Fig. %d',figure_nr);

        ts = sprintf('%.3f',x);
        ts = sprintf('%.3f',fun(real(x),0.6));
        axis square

switch offset_corrected
    case 0
    case 1
        text(-0.5,4.35,'(sim_{in})^c - mean subtraced','FontSize',22,'Color','black');
    case 2
    case 3
    case 4
    case 5