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 noise_bands = identify_fft_noise (f, fft, start_freq, window_hertz, spike_threshold_multiplier)

start_freq; % Only search for noise at frequencies above this one
window_hertz; % window size measured in measured in hertz
spike_threshold_multiplier; % Consider it a spike if it is this many times larger than the average surrounding power

%Convert this to the indicies of f
df = f(2) - f(1);
window = round(window_hertz / df);

%Change FFT coefficients to power spectrum
powerSpec = (abs(fft).^2);

noise_bands = [];
start_loc = find (f >= start_freq, 1, 'first'); % Identify location of starting frequency
for i = start_loc:window:(length(f)-window)
    meanpower = mean(abs(powerSpec(i:(i+window-1))));  %Approxixmate power of power spectrum for this particular window
    noise_bands = [noise_bands (find(powerSpec(i:(i+window-1)) > (spike_threshold_multiplier*meanpower)) + (i-1))];   %Find if there are any spikes where the power is many times greater than the background noise

noise_bands = noise_bands * df;


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