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_prior_responses.m
    path of file: ./repos/bspm/thirdparty/spm12/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.
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_dcm_prior_responses.m 6917 2016-11-02 14:25:08Z karl $


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


% models
%--------------------------------------------------------------------------
model = {'ERP','SEP','LFP','CMC','TFM','CMM','NMM','MFM'};
m
    
function and ERP
    %----------------------------------------------------------------------
    [csd, Hz ] = spm_csd_mtf(pE,M,[]);
    [ccf, lag] = spm_csd2ccf(csd,Hz);
    [erp, pst] = spm_gen_erp(pE,M,U);  
    
    
    % plot
    %----------------------------------------------------------------------
    lag = 1000*lag;
    pst = 1000*pst;
    
    subplot(3,3,3*iplot + 2)
    plot(Hz,abs(csd{1})),  hold on
    
    subplot(3,3,3*iplot + 3)
    plot(lag,ccf{1}), hold on
    
    
    % now repeat without channel noise
    %----------------------------------------------------------------------
    pE.b = pE.b - 32;
    pE.c = pE.c - 32;
    [csd, Hz]  = spm_csd_mtf(pE,M,[]);
    [ccf, lag] = spm_csd2ccf(csd,Hz);
    
    % plot
    %----------------------------------------------------------------------
    subplot(3,3,3*iplot + 2)
    plot(Hz,abs(csd{1}),'b--'), hold off
    xlabel('frequency {Hz}')
    title('autospectrum','FontSize',16)
    
    subplot(3,3,3*iplot + 3)
    plot(lag,ccf{1},'b--'), hold off
    title('autocovariance','FontSize',16)
    
    subplot(3,3,3*iplot + 1), hold off
    plot(pst,erp{1},'b'), hold on
    title(sprintf('ERP: %s',model{i}),'FontSize',16)
    
    % hidden states
    %----------------------------------------------------------------------
    M.g  = @(x,u,P,M) x;
    erp  = spm_gen_erp(pE,M,U);
    plot(pst,erp{1}/32,'r:'), hold off
    
end