L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)

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This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model.
1 . Li X, Morita K, Robinson HP, Small M (2013) Control of layer 5 pyramidal cell spiking by oscillatory inhibition in the distal apical dendrites: a computational modeling study. J Neurophysiol 109:2739-56 [PubMed]
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: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I K,Ca; I Na, leak;
Gap Junctions:
Receptor(s): AMPA;
Transmitter(s): Dopamine;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Intrinsic plasticity;
Implementer(s): Moradi, Keivan [k.moradi at gmail.com]; Robinson, H.P.C. [hpcr at cam.ac.uk]; Small, Michael ; Li, Xiumin ;
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; AMPA; I K,Ca; I Na, leak; Dopamine;
function Mccum_Gaussian
global delta_2

% global amp
% amp=0.3;
% a code to make cumulative distribution functions for RS and FS based on
%%%%%%%%%% create cumulative distribution functions for RS and FS
%%%%%%%%%% %%%%%%%%%%
pp_cumulative_FS = Mccum_main(2,1/50);
FS_spline = pp_cumulative_FS; % re-naming
%%%%%%%%%% create spline representation of the inverse function of the cumulative distribution functions %%%%%%%%%%
tmpx = [0:0.001:1];
tmpyFS = ppval(FS_spline,tmpx);
FS_inv_spline = spline(tmpyFS,tmpx);
save(['GABAspline_Gaussian_delta2' '_' num2str(delta_2) '.mat'], 'FS_inv_spline');
% save(['GABAspline_Sin_amp' '_' num2str(amp) '.mat'], 'FS_inv_spline');
%%%%%%%%%% inner-file functions %%%%%%%%%%

function pp_cumulative = Mccum_main(RSorFS,filtersigma)
global delta_2
% global amp
sampleinterval = 0.001;
x = [0:sampleinterval:1];
% plot to check
if 1
%     y = GABA_density(0,delta_2,x);
% %     y = GABA_density_sin(amp,x);
%     F_density = figure;
%     A_density = axes;
%     hold on;
%     tmp = plot([0:sampleinterval:2],[y(1:end-1) y],'k'); set(tmp,'LineWidth',1); % after filtering and spline
% %     axis([0 2 0 0.9]);
%     set(A_density,'Box','on');
%     set(A_density,'XTick',[0:0.5:2]);
%     set(A_density,'YTick',[0:0.2:0.9]);

%%% making cumulative distribution function by integrating the above probability density

% numerical integration
tmp = zeros(1,length(x));
for j = 1:length(x)
    fprintf('I''m now numerically integrating at %d\n',j);
    tmp(j) = quad(@evalspline, 0, x(j), 1.0e-8); % numerically integrate 'evalspline' with the 2nd input variable 'pp_density_wonorm' (NB: 'pp_density_wonorm' should be 6th input of 'quad')
y_cumulative = tmp / tmp(end); % division by 'tmp(end)' is for normalization to have a value 1 at the end.
% NB: The above 'y_cumulative' on the below 'x' is a final fitting (but before spline) of the cumulative distribution
pp_cumulative = spline(x,y_cumulative); % make a spline approximation of the density function 'y_cumulative on x'
% so now the function 'ppval(pp_cumulative,x)', or equivalently, 'evalspline(x,pp_cumulative)' gives the ultimately final splined version of the cumulative distribution function

% plot to check
if 1
%     z = ppval(pp_cumulative,x);
%     F_cumulative = figure;
%     A_cumulative = axes;
%     hold on;
%     tmp = plot([0:sampleinterval:2],[z(1:end-1) 1+z],'k'); set(tmp,'LineWidth',1); % plot repeats to check the smoothness of the connection
% %     axis([0 2 0 2]);
%     set(A_cumulative,'Box','on');
% %     set(A_cumulative,'XTick',[0:0.5:2]);
% %     set(A_cumulative,'YTick',[0:0.5:2]);

function y = evalspline(x)
global delta_2
% global amp
y = GABA_density(0,delta_2,x);
% y = GABA_density_sin(amp,x);

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