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_dcm_x_neural_NMDA.m
    path of file: ./repos/spm/toolbox/Neural_Models/NMDA_NMM_MFM
    
    the code of the file until where you have to start completion: function [x,f] = spm_dcm_x_neural_NMDA(P,model)
% Return the state and equation of neural mass models
% FORMAT [x,f] = spm_dcm_x_neural_NMDA(P,'model')
%
%  P      - parameter structure
% 'model' - 'ERP','SEP','LFP','NNM' or 'MFM'
%
% x   - initial states
% f   - state euquation
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


% initial state and equation
%--------------------------------------------------------------------------
switch lower(model)

    % linear David et al model (linear in states)
    %======================================================================
    case{'erp'}

        % inital states and equations of motion

    
function [x,f] = spm_dcm_x_neural_NMDA(P,model)
% Return the state and equation of neural mass models
% FORMAT [x,f] = spm_dcm_x_neural_NMDA(P,'model')
%
%  P      - parameter structure
% 'model' - 'ERP','SEP','LFP','NNM' or 'MFM'
%
% x   - initial states
% f   - state euquation
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


% initial state and equation
%--------------------------------------------------------------------------
switch lower(model)

    % linear David et al model (linear in states)
    %======================================================================
    case{'erp'}

        % inital states and equations of motion
        %------------------------------------------------------------------
        x  =  spm_x_erp(P);
        f  = 'spm_fx_erp';


    % linear David et al model (linear in states) - fast version for SEPs
    %======================================================================
    case{'sep'}

        % inital states
        %------------------------------------------------------------------
        x  =  spm_x_erp(P);
        f  = 'spm_fx_erp';
        
    % linear David et al model (linear in states) - with self-inhibition
    %======================================================================
    case{'lfp'}

        % inital states
        %------------------------------------------------------------------
        x  =  spm_x_lfp(P);
        f  = 'spm_fx_lfp';


    % Neural mass model (nonlinear in states)
    %======================================================================
    case{'nmm'}

        % inital states and model
        %------------------------------------------------------------------
        x  = spm_x_nmm_NMDA(P);
        f  = 'spm_fx_mfm_NMDA';


    % Mean field model (nonlinear in states) - with covariance
    %======================================================================
    case{'mfm'}

        % inital states and model
        %------------------------------------------------------------------
        x  = spm_x_mfm_NMDA(P);
        f  = 'spm_fx_mfm_NMDA';
        

    otherwise
        warndlg('Unknown model')
end