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_mfa_G.m
    path of file: ./repos/spm12/toolbox/Neural_Models
    
    the code of the file until where you have to start completion: function [G] = spm_mfa_G(M,x)
% creates a structure for a Gibb's ensemble
% FORMAT [G] = spm_mfa_G(M,x)
%--------------------------------------------------------------------------
% M   - model specification structure
% Required fields:
%    M.f   - dx/dt    = f(x,u,P)                {function string or m-file}
%    M.g   - y(t)     = l(x,P)                  {function string or m-file}
%    M.m   - m inputs
%    M.n   - n states
%    M.l   - l ouputs
%    M.x   - (n x 1) = x(0) = expansion point
%    M.W   - (n x n) - covariance matrix of deterministic noise
% x    -  {1 x d cell}    - range of state [d]-space
%
% G   - ensemble specification structure
% fields
%   G.M:  [1 x 1 struct]  - dynamic model structure
%   G.J0: [n x n double]  - Jacobian
%   G.J1: {1 x M.m cell}  - dJ0/du
%   G.L : [l x n double]  - d<y>/dp
%   G.u:  [n x m double]  - probability modes
%   G.v:  [m x n double]  - v*u = 1
%   G.X:  [n x d double]  - evaluation points of state [d]-space
%   G.x:  {1 x d cell}    - range of state [d]-space
%   G.p0: [n x 1 sparse]  - expansion point
%   G.q0: [n x 1 sparse]  - equilibrium density
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

    
function [G] = spm_mfa_G(M,x)
% creates a structure for a Gibb's ensemble
% FORMAT [G] = spm_mfa_G(M,x)
%--------------------------------------------------------------------------
% M   - model specification structure
% Required fields:
%    M.f   - dx/dt    = f(x,u,P)                {function string or m-file}
%    M.g   - y(t)     = l(x,P)                  {function string or m-file}
%    M.m   - m inputs
%    M.n   - n states
%    M.l   - l ouputs
%    M.x   - (n x 1) = x(0) = expansion point
%    M.W   - (n x n) - covariance matrix of deterministic noise
% x    -  {1 x d cell}    - range of state [d]-space
%
% G   - ensemble specification structure
% fields
%   G.M:  [1 x 1 struct]  - dynamic model structure
%   G.J0: [n x n double]  - Jacobian
%   G.J1: {1 x M.m cell}  - dJ0/du
%   G.L : [l x n double]  - d<y>/dp
%   G.u:  [n x m double]  - probability modes
%   G.v:  [m x n double]  - v*u = 1
%   G.X:  [n x d double]  - evaluation points of state [d]-space
%   G.x:  {1 x d cell}    - range of state [d]-space
%   G.p0: [n x 1 sparse]  - expansion point
%   G.q0: [n x 1 sparse]  - equilibrium density
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_mfa_G.m 4936 2012-09-18 19:47:55Z karl $
 
% Bilinear Mean field form
%--------------------------------------------------------------------------
[J0,J1,L,X,q0] = spm_mfa(M,x);
 
% find modes (remove unstable modes)
%--------------------------------------------------------------------------
if length(J0) < 1024
    [u s] = eig(full(J0));
else
    [u s] = eigs(J0,65,'LR');
end