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_ssr_priors.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8/toolbox/dcm_meeg
    
    the code of the file until where you have to start completion: function [pE,pC] = spm_ssr_priors(pE,pC)
% augments prior moments of a neural mass model for ssr analyses
% FORMAT [pE,pC] = spm_ssr_priors(pE,pC)
%
% pE - prior expectation
%
% adds
%
% input and noise parameters

    
function [pE,pC] = spm_ssr_priors(pE,pC)
% augments prior moments of a neural mass model for ssr analyses
% FORMAT [pE,pC] = spm_ssr_priors(pE,pC)
%
% pE - prior expectation
%
% adds
%
% input and noise parameters
%--------------------------------------------------------------------------
%    pE.a - amplitude of AR  component
%    pE.b - amplitude of IID component
%    pE.c - amplitude of AR  noise (channel specific and non-specific)
%    pE.d - amplitude of IID noise (channel specific and non-specific)
%
%--------------------------------------------------------------------------
%
% pC - prior (co)variances
%
% Because priors are specified under log normal assumptions, most
% parameters are simply scaling coefficients with a prior expectation
% and variance of one.  After log transform this rend