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 compare_fft (s1, s2, minfreq, maxfreq)

format compact;

figure;



% Do it for s1

s = s1;

df = s.fft.f(2) - s.fft.f(1);
% Get Normalizing constant
start = find (s.fft.f>=minfreq,1);
stop = find (s.fft.f>=maxfreq,1);
cnorm = sum (abs(s.fft.fft_val(start:stop)).^2) * df;


% Plot FFT Spectrum
% temp = round(length(t1)/2);
temp = round(length(s.fft.f));
h1 = loglog ((s.fft.f(2:temp)), (abs(s.fft.fft_val(2:temp)).^2) / cnorm, 'b'); hold on;
hold on
%h1 = loglog ((s.fft.f(s.fft.fitlist)),(abs(s.fft.fft_val(s.fft.fitlist)).^2) / cnorm,'g:'); hold on;   %loglogs the region of the spectrum we're fitting to
% temp2 = round(length(s.noisetimes)/2);
% loglog (s.fftnoise.f(2:temp2), abs(s.fftnoise.fft_val(2:temp2)).^2, 'r');
%loglog starting at 2 to remove the zero term.
title('Power Spectrum');
xlabel ('freq (hz)')



%loglog linear best fit
fitlist = s.fft.fitlist;
p = [s.general_beta_est.beta_est s.general_beta_est.const_est];         %New format
% p = [s.general_beta_est, log10(mean(abs(s.fft.fft_val(2:temp).^2)))]; %Uncomment for old format
h2 = loglog((s.fft.f(min(fitlist):temp)), (10^p(2) * s.fft.f(min(fitlist):temp).^p(1)) / cnorm, 'r');
legend ([h1 h2], 'Fitting region',['Fit slope = ' num2str(p(1),'%1.2f')], 'location', 'NorthWest');

axis ([0 500 0 max(abs(s.fft.fft_val(2:temp)).^2)/cnorm]);


% Do it for s2
s = s2;
% Get Normalizing constant
start = find (s.fft.f>=minfreq,1);
stop = find (s.fft.f>=maxfreq,1);
cnorm = sum (abs(s.fft.fft_val(start:stop)).^2) * df;


% loglog FFT Spectrum
% temp = round(length(t1)/2);
temp = round(length(s.fft.f));
h1 = loglog ((s.fft.f(2:temp)), (abs(s.fft.fft_val(2:temp)).^2) / cnorm, 'r'); hold on;
%h1 = loglog ((s.fft.f(s.fft.fitlist)),(abs(s.fft.fft_val(s.fft.fitlist)).^2) / cnorm,'g:'); hold on;   %loglogs the region of the spectrum we're fitting to
% temp2 = round(length(s.noisetimes)/2);
% loglog (s.fftnoise.f(2:temp2), abs(s.fftnoise.fft_val(2:temp2)).^2, 'r');
%loglog starting at 2 to remove the zero term.
title('Power Spectrum');
xlabel ('freq (hz)')



%loglog linear best fit
fitlist = s.fft.fitlist;
p = [s.general_beta_est.beta_est s.general_beta_est.const_est];         %New format
% p = [s.general_beta_est, log10(mean(abs(s.fft.fft_val(2:temp).^2)))]; %Uncomment for old format
h2 = loglog((s.fft.f(min(fitlist):temp)), (10^p(2) * s.fft.f(min(fitlist):temp).^p(1)) / cnorm, 'm');
legend ([h1 h2], 'Fitting region',['Fit slope = ' num2str(p(1),'%1.2f')], 'location', 'NorthWest');

axis ([0 500 0 max(abs(s.fft.fft_val(2:temp)).^2)/cnorm]);


hold off                          

end





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