CA1 pyramidal: Stochastic amplification of KCa in Ca2+ microdomains (Stanley et al. 2011)

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This minimal model investigates stochastic amplification of calcium-activated potassium (KCa) currents. Amplification results from calcium being released in short high amplitude pulses associated with the stochastic gating of calcium channels in microdomains. This model predicts that such pulsed release of calcium significantly increases subthreshold SK2 currents above what would be produced by standard deterministic models. However, there is little effect on a simple sAHP current kinetic scheme. This suggests that calcium stochasticity and microdomains should be considered when modeling certain KCa currents near subthreshold conditions.
1 . Stanley DA, Bardakjian BL, Spano ML, Ditto WL (2011) Stochastic amplification of calcium-activated potassium currents in Ca2+ microdomains. J Comput Neurosci 31:647-66 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I K,Ca; I_AHP;
Gap Junctions:
Gene(s): KCa2.2 KCNN2;
Simulation Environment: MATLAB;
Model Concept(s): Ion Channel Kinetics; Active Dendrites; Detailed Neuronal Models; Calcium dynamics; Noise Sensitivity; Markov-type model;
Implementer(s): Stanley, David A ;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; I K,Ca; I_AHP;
% Like sAHP6s, except disregard high-rate transitions between C and O
% for increased processing time

function [t y] = sAHP5s (p)

plot_on = 0;

% % % Period, duty cycle of Ca, and total sim time
% p.factor = 2;
% p.rate_scale = 10.0;
% p.ti = 0;
% = 1;
% p.per = 0.02;
% p.dc = 1.0;
% p.Ca_level = 50e-9;

p.per = p.per * p.factor;
p.dc = p.dc / p.factor;
p.Ca_level = p.Ca_level * p.factor;
t0 = [];
Ca_mean = p.Ca_level*p.dc;

% % Rates
rb = 10e6*300/750*p.rate_scale;
ru = 0.5*p.rate_scale;
p.alpha1 = 4*rb;
p.beta1 = 1*ru;
p.alpha2 = 3*rb;
p.beta2 = 2*ru;
p.alpha3 = 2*rb;
p.beta3 = 3*ru;
p.alpha4 = 1*rb;
p.beta4 = 4*ru;

% %  Initial Conditions
xsing_inf = Ca_mean*rb / (Ca_mean*rb + ru);
x1inf = 1*(1-xsing_inf)^4*(xsing_inf)^0;
x2inf = 4*(1-xsing_inf)^3*(xsing_inf)^1;
x3inf = 6*(1-xsing_inf)^2*(xsing_inf)^2;
x4inf = 4*(1-xsing_inf)^1*(xsing_inf)^3;
% x5inf = 1*(1-xsing_inf)^0*(xsing_inf)^4;
y0 = [x1inf x2inf x3inf x4inf];

options = odeset('AbsTol', 1e-9, 'RelTol', 1e-6, 'MaxStep', max(p.per*p.dc/10,2.5e-5));
[t yarr] = ode45(@sAHP5s_eqn, t0, y0, options, p);
y = 1-sum(yarr,2);

if plot_on; figure; plot(t,[yarr y]); legend('1','2','3','4','5'); end
if plot_on; figure; plot(t,y); end