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 [const_est beta_est fit_list] = fit_betastandard_scaling (f, fft_val, intlist)
global a_scale

intlist = sortrows (intlist);
[row col] = size(intlist);
if intlist(row,1) > (max(f))
    'Warning: Fitting values above Nyquest frequency. Consider adjusting intlist'
df = f(2) - f(1);

%For curve fitting, we need to exclude the regions of the graph that have been filtered
    %Find the indicies that have been filtered and therefore need to be excluded
    exclude_list = [];
    for i = 1:(size(intlist, 1))
        exclude_list = [exclude_list find(f >= (intlist(i,1)-df) & f <= (intlist(i,2)+df) )];
    % Use this trick to obtain the "inverse" list 
    unfiltered_index = 1:length(f);
    unfiltered_index(exclude_list) = -1;
    fit_list = find (unfiltered_index >= 0);

% p = polyfit(log10(f(fit_list)), log10(abs(fft_val(fit_list)).^2),1);
% beta_est = p(1);
% const_est = p(2);

fft_power = abs(fft_val).^2;
a = interp1(f, fft_power, 1);
b = -2;
aprime = abs(b);            % Make aprime on the order of b
a_scale = a / aprime;
coefs0 = [aprime b];
[coefs err] = lsqcurvefit (@myfunc, coefs0, f(fit_list), fft_power(fit_list), -Inf, Inf);
aprime = coefs(1);
a = aprime * a_scale;
const_est = log10(a);          %Take log to get it in the same form as loglog curve fitting
beta_est = coefs(2);


function pow = myfunc(coefs, f)
global a_scale
% Multiscale power function of form power = f^-beta

    aprime = coefs(1);
    a = aprime * a_scale;
    b = coefs(2);
    pow = a*f.^b;


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