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    Metadata details:
    filename: spm_dcm_prior_responses.m
    path of file: ./repos/spm/toolbox/Neural_Models
    
    the code of the file until where you have to start completion: function spm_dcm_prior_responses(Ep)
% Demo routine that computes transfer functions for free parameters
%==========================================================================
%
% This routine provides a survey of responses under stationarity
% assumptions for the suite of neural mass and mean field models used in
% DCM. It characterises the steady-state responses - under prior
% expectations - using spectral density and autocovariance functions
% with and with out channel noise. it then proceeds to evaluate evoked
% responses to a canonical input.
%
% This function is used primarily to check the prior expectations to ensure
% the expected responses within a comparable and appropriate range for
% scale empirical data. The amplitude of the responses are set by the
% scaling of U in the equations of motion for each model.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


% Model specification
%==========================================================================


% models
%--------------------------------------------------------------------------
model = {'ERP','SEP','LFP','CMC','TFM','CMM','NMM','MFM'};
model = model(:);
Nm    = length(model);

% spatial model and sources
%--------------------------------------------------------------------------
M.dipfit.type  = 'LFP';
M.dipfit.Nc    = 1;
M.dipfit.Ns    = 1;


% within-trial effects: adjust onset relative to pst
%----------------------------------------------------------------------
M.ns   = 64;
M.ons  = 64;
M.dur  = 16;
U.dt   = 0.004;
U.X    = [];

    
function spm_dcm_prior_responses(Ep)
% Demo routine that computes transfer functions for free parameters
%==========================================================================
%
% This routine provides a survey of responses under stationarity
% assumptions for the suite of neural mass and mean field models used in
% DCM. It characterises the steady-state responses - under prior
% expectations - using spectral density and autocovariance functions
% with and with out channel noise. it then proceeds to evaluate evoked
% responses to a canonical input.
%
% This function is used primarily to check the prior expectations to ensure
% the expected responses within a comparable and appropriate range for
% scale empirical data. The amplitude of the responses are set by the
% scaling of U in the equations of motion for each model.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


% Model specification
%==========================================================================


% models
%--------------------------------------------------------------------------
model = {'ERP','SEP','LFP','CMC','TFM','CMM','NMM','MFM'};
model = model(:);
Nm    = length(model);

% spatial model and sources
%--------------------------------------------------------------------------
M.dipfit.type  = 'LFP';
M.dipfit.Nc    = 1;
M.dipfit.Ns    = 1;


% within-trial effects: adjust onset relative to pst
%----------------------------------------------------------------------
M.ns   = 64;
M.ons  = 64;
M.dur  = 16;
U.dt   = 0.004;
U.X    = [];

ifig  = 1;
for i = 1:Nm
    
    
    % dear figure and plot
    %======================================================================
    iplot = rem(i - 1,3);
    if iplot == 0
        spm_figure('GetWin',sprintf('stationary responses: %i',ifig));
        ifig = ifig + 1;
    end