You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_fx_cmc_2014.m
    path of file: ./repos/spm12/toolbox/dcm_meeg
    
    the code of the file until where you have to start completion: function [f,J,Q] = spm_fx_cmc_2014(x,u,P,M)
% state equations for a neural mass model (canonical microcircuit)
% FORMAT [f,J,D] = spm_fx_cmc(x,u,P,M)
% FORMAT [f,J]   = spm_fx_cmc(x,u,P,M)
% FORMAT [f]     = spm_fx_cmc(x,u,P,M)
% x      - state vector
%   x(:,1) - voltage     (spiny stellate cells)
%   x(:,2) - conductance (spiny stellate cells)
%   x(:,3) - voltage     (superficial pyramidal cells)
%   x(:,4) - conductance (superficial pyramidal cells)
%   x(:,5) - current     (inhibitory interneurons)
%   x(:,6) - conductance (inhibitory interneurons)
%   x(:,7) - voltage     (deep pyramidal cells)
%   x(:,8) - conductance (deep pyramidal cells)
%
% f        - dx(t)/dt  = f(x(t))
% J        - df(t)/dx(t)
% D        - delay operator dx(t)/dt = f(x(t - d))
%                                    = D(d)*f(x(t))
%
% Prior fixed parameter scaling [Defaults]
%
% E  = (forward, backward, lateral) extrinsic rates 
% G  = intrinsic rates

    
function [f,J,Q] = spm_fx_cmc_2014(x,u,P,M)
% state equations for a neural mass model (canonical microcircuit)
% FORMAT [f,J,D] = spm_fx_cmc(x,u,P,M)
% FORMAT [f,J]   = spm_fx_cmc(x,u,P,M)
% FORMAT [f]     = spm_fx_cmc(x,u,P,M)
% x      - state vector
%   x(:,1) - voltage     (spiny stellate cells)
%   x(:,2) - conductance (spiny stellate cells)
%   x(:,3) - voltage     (superficial pyramidal cells)
%   x(:,4) - conductance (superficial pyramidal cells)
%   x(:,5) - current     (inhibitory interneurons)
%   x(:,6) - conductance (inhibitory interneurons)
%   x(:,7) - voltage     (deep pyramidal cells)
%   x(:,8) - conductance (deep pyramidal cells)
%
% f        - dx(t)/dt  = f(x(t))
% J        - df(t)/dx(t)
% D        - delay operator dx(t)/dt = f(x(t - d))
%                                    = D(d)*f(x(t))
%
% Prior fixed parameter scaling [Defaults]
%
% E  = (forward, backward, lateral) extrinsic rates 
% G  = intrinsic rates
% D  = propagation delays (intrinsic, extrinsic)
% T  = synaptic time constants
% R  = slope of sigmoid activation function
%
%__________________________________________________________________________
% David O, Friston KJ (2003) A neural mass model for MEG/EEG: coupling and
% neuronal dynamics. NeuroImage 20: 1743-1755
%___________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_fx_cmc_2014.m 7279 2018-03-10 21:22:44Z karl $
 
 
% get dimensions and configure state variables
%--------------------------------------------------------------------------
x  = spm_unvec(x,M.x);                % neuronal states
n  = size(x,1);                       % number of sources


% [default] fixed parameters
%--------------------------------------------------------------------------
E  = [1 1/8 1/4 1/2]*200;             % extrinsic (forward and backward)  
G  = [4 4 8 4 4 2 4 4 2 2 2 4]*200;   % intrinsic connections
T  = [2 2 16 28];                     % synaptic time constants
R  = 1;                               % slope of sigmoid activation function

% NB for more pronounced state depend