Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)

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Accession:142104
This model produces the hippocampal CA3 neural network model used in the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. This model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS 2.3 to implement parameter sweeps. The model contains four cell types derived from prior papers. CA3 pyramidal are derived from Traub et al (1991); Basket, stratum oriens (O-LM), and Medial Septal GABAergic (MSG) interneurons are taken from Hajos et al (2004).
Reference:
1 . Stanley DA, Talathi SS, Parekh MB, Cordiner DJ, Zhou J, Mareci TH, Ditto WL, Carney PR (2013) Phase shift in the 24-hour rhythm of hippocampal EEG spiking activity in a rat model of temporal lobe epilepsy. J Neurophysiol 110:1070-86 [PubMed]
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: Hippocampus; Medial Septum;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron; Hippocampus septum medial GABAergic neuron;
Channel(s): I Na,t; I A; I K; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS; MATLAB;
Model Concept(s): Epilepsy; Brain Rhythms; Circadian Rhythms;
Implementer(s): Stanley, David A ;
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; GabaA; AMPA; I Na,t; I A; I K; I h; I K,Ca; I Calcium; Gaba; Glutamate;

function handles = plot_matrix (t, data, opt_strct, leg_arr, colourarr, linesize)

if nargin < 6
   linesize = 1; 
end

if nargin < 5
    colourarr = ['bgrmckbgrmckbgrmckbgrmckbgrmckbgrmck'];
    colourarr = get(0, 'DefaultAxesColorOrder'); colourarr = repmat(colourarr, size(data,2), 1);
end

if isempty(colourarr); colourarr = ['bgrmckbgrmckbgrmckbgrmckbgrmckbgrmck']; end

ds = 1;
shift = 0;
zero_means = 0;
plotloglog = 0;
normalize_everything = 0;

if exist('opt_strct','var')
    if ~isempty(opt_strct);
        if isfield (opt_strct,'ds'); ds = opt_strct.ds; end
        if isfield (opt_strct,'shift'); shift = opt_strct.shift; end
        if isfield (opt_strct,'zero_means'); zero_means = opt_strct.zero_means; end
        if isfield (opt_strct,'plotloglog'); plotloglog = opt_strct.plotloglog; end
        if isfield (opt_strct,'normalize_everything'); normalize_everything = opt_strct.normalize_everything; end
    end
end

if normalize_everything
   for jj = 1:size(data,2)
       xtemp = data(:,jj);
       xtemp = xtemp - mean(xtemp);
       xtemp = xtemp / std(xtemp);
       data(:,jj) = xtemp;
   end
end

t = downsample(t, ds);
for i = 1:size(data,2)
    if ischar(colourarr)
        currcolour = colourarr(i);
    else
        currcolour = colourarr(i,:);
    end
    plotdata = data(:,i);
    if zero_means; plotdata = plotdata - mean(plotdata); end
    plotdata = downsample(plotdata, ds);
    if ~plotloglog
        handles.plot = plot (t, plotdata + (i-1)*shift, 'Color', currcolour, 'LineWidth',linesize); hold on;
    else
        loglog(t, plotdata, 'Color', currcolour, 'LineWidth',linesize); hold on;
        if (shift ~= 0)
            handles.plotlog = loglog(t, plotdata + (i-1)*shift, 'Color', currcolour, 'LineWidth',linesize); hold on;
        end
    end
end

if exist('leg_arr','var')
    if ~isempty(leg_arr)
        for i = 1:length(leg_arr)
            tempstr = leg_arr{i};
            leg_arr{i} = strrep (leg_arr{i}, '_', ' ');
        end
        handles.legend = legend (leg_arr);
    end
end




end

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