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_cmm_NMDA.m
    path of file: ./repos/spm/toolbox/dcm_meeg
    
    the code of the file until where you have to start completion: function [f,J,Q] = spm_fx_cmm_NMDA(x,u,P,M)
% State equations for canonical neural-mass and mean-field models
% FORMAT [f,J,Q] = spm_fx_cmm(x,u,P,M)
%
% x - states and covariances
%
% x(i,j,k)        - k-th state of j-th population of i-th source
%                   i.e., running over sources, pop. and states
%
%   population: 1 - excitatory spiny stellate cells (input cells)
%               2 - superficial pyramidal cells     (forward output cells)
%               3 - inhibitory interneurons         (intrisic interneurons)
%               4 - deep pyramidal cells            (backward output cells)
%
%        state: 1 V  - voltage
%               2 gE - conductance (excitatory)
%               3 gI - conductance (inhibitory)
%
%--------------------------------------------------------------------------
% refs:
%
% Marreiros et al (2008) Population dynamics under the Laplace assumption
%
% See also:
%
% Friston KJ.

    
function [f,J,Q] = spm_fx_cmm_NMDA(x,u,P,M)
% State equations for canonical neural-mass and mean-field models
% FORMAT [f,J,Q] = spm_fx_cmm(x,u,P,M)
%
% x - states and covariances
%
% x(i,j,k)        - k-th state of j-th population of i-th source
%                   i.e., running over sources, pop. and states
%
%   population: 1 - excitatory spiny stellate cells (input cells)
%               2 - superficial pyramidal cells     (forward output cells)
%               3 - inhibitory interneurons         (intrisic interneurons)
%               4 - deep pyramidal cells            (backward output cells)
%
%        state: 1 V  - voltage
%               2 gE - conductance (excitatory)
%               3 gI - conductance (inhibitory)
%
%--------------------------------------------------------------------------
% refs:
%
% Marreiros et al (2008) Population dynamics under the Laplace assumption
%
% See also:
%
% Friston KJ.
% The labile brain. I. Neuronal transients and nonlinear coupling. Philos
% Trans R Soc Lond B Biol Sci. 2000 Feb 29;355(1394):215-36. 
% 
% McCormick DA, Connors BW, Lighthall JW, Prince DA.
% Comparative electrophysiology of pyramidal and sparsely spiny stellate
% neurons of the neocortex. J Neurophysiol. 1985 Oct;54(4):782-806.
% 
% Brunel N, Wang XJ.
% What determines the frequency of fast network oscillations with irregular
% neural discharges? I. Synaptic dynamics and excitation-inhibition
% balance. J Neurophysiol. 2003 Jul;90(1):415-30.
% 
% Brunel N, Wang XJ.
% Effects of neuromodulation in a cortical network model of object working
% memory dominated by recurrent inhibition. J Comput Neurosci. 2001
% Jul-Aug;11(1):63-85.
%
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2005-2022 Wellcome Centre for Human Neuroimaging

 
% get dimensions and configure state variables
%--------------------------------------------------------------------------
ns   = size(M.x,1);                      % number of sources
np   = size(M.x,2);                      % number of populations per source
nk   = size(M.x,3);                      % number of states per population
x    = reshape(x,ns,np,nk);              % hidden states 


% extrinsic connection strengths
%==========================================================================
 
% exponential transform to ensure positivity constraints
%--------------------------------------------------------------------------
A{1}  = exp(P.A{1});                      % forward
A{2}  = exp(P.A{2});                      % backward
AN{1} = exp(P.AN{1});                      % forward
AN{2} = exp(P.AN{2});                      % backward
C     = exp(P.C);                         % subcortical
 

% detect and reduce the strength of reciprocal (lateral) connections
%--------------------------------------------------------------------------
for i = 1:length(A)
    L    = (A{i} > exp(-8)) & (A{i}' > exp(-8));
    A{i} = A{i}./(1 + 8*L);
end