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

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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).
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;
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 betas_plot(s, display_power)

len = length (s.data);
numcoefs = length (s.wvstruct.dwt);

%Plot a comparison fft and the betas power spectra
if display_power == 1

    %Plot Wavelet Spectrum
    loglog ((s.fft.wvf), (abs(s.fft.wvfft_val.^2)), 'b'); hold on;
    h3 = loglog ((s.fft.wvf(s.fft.wvfitlist)),(abs(s.fft.wvfft_val(s.fft.wvfitlist)).^2),'g:'); hold on;   %Plots the region of the spectrum we're fitting to
    title('Power Spectrum (datafilt)');
    xlabel ('freq (hz)')

    % Plot linear best fit
    temp = length(s.fft.wvf);
    wvfitlist = s.fft.wvfitlist;
    p = [s.general_beta_est.wvbeta_est s.general_beta_est.wvconst_est];         %New format
    h4 = loglog((s.fft.wvf(min(wvfitlist):temp)), (10^p(2) * s.fft.wvf(min(wvfitlist):temp).^p(1)), 'm');
    legend ([h3 h4], ['Wavelet Spectrum'],['Fit slope = ' num2str(p(1),'%1.2f')], 'location', 'NorthWest');


%Plot the betas
bar (fliplr(s.betas.b(2,2:size(s.betas.b,2))));

%Add text and axis labels
for i = 2:numcoefs
    %Calculate frequencies and store in array to be placed along x-axis
    %xlabel_arr(i-1) = {[num2str(1/(2^(i-1))/s.dt1,'%1.1f') '-' num2str(1/(2^i)/s.dt1,'%1.1f')]};
    xlabel_arr(i-1) = {[num2str(1/(2^i)/s.dt1,'%1.1f')]};

    %Also draw in the scales
    ypos = max(s.betas.b(2,2:size(s.betas.b,2))) * 1;
    %text(numcoefs-(i-1)-.5, ypos, ['Scl' num2str(2^(i-1)) '-' num2str(2^i)],'FontSize',8);
    text(numcoefs-(i-1)-.5, ypos, ['Scl' num2str(2^(i-1))],'FontSize',8);

xlabel_arr = fliplr (xlabel_arr);
set(gca,'XTickLabel',xlabel_arr, 'FontSize', 8)
title('Multiscale Exponents (data)');
xlabel ('Hz');


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