2023_Blaschke_Stroke_tDCS_rsfMRI/scripts/toolbox/spm12/spm_nlsi.m

function varargout = spm_nlsi(M,U,Y)
% nonlinear system identification of a MIMO system
% FORMAT [Ep,Cp,Eh,K0,K1,K2,M0,M1,L1,L2] = spm_nlsi(M,U,Y)
% FORMAT [K0,K1,K2,M0,M1,L1,L2]          = spm_nlsi(M)
%
% Model specification
%--------------------------------------------------------------------------
% M.f     - dx/dt = f(x,u,P,M)  {function string or m-file}
% M.g     - y     = g(x,u,P,M)  {function string or m-file}
%
% M.pE    - (p x 1)   Prior expectation of p model parameters
% M.pC    - (p x p)   Prior covariance for p model parameters
%
% M.x     - (n x 1)   intial state x(0)
% M.m     - m         number of inputs
% M.n     - n         number of states
% M.l     - l         number of outputs
% M.N     -           kernel depth
% M.dt    -           kernel resolution {secs}
%
% System inputs
%--------------------------------------------------------------------------
% U.u   - (v x m)   m inputs
% U.dt  -           sampling interval for inputs
%
% System outputs
%--------------------------------------------------------------------------
% Y.y   - (v x l)   l outputs
% Y.X0  - (v x c)   Confounds or null space
% Y.dt  -           sampling interval for outputs
% Y.Q   -           observation error precision components
%
% Model Parameter estimates - conditional moments
%--------------------------------------------------------------------------
% Ep    - (p x 1)           conditional expectation  E{P|y}
% Cp    - (p x p)           conditional covariance   Cov{P|y}
% Eh    - (v x v)           conditional log-precision
%
% System identification     - Volterra kernels
%--------------------------------------------------------------------------
% K0    - (l x 1)             = k0(t)         = y(t)
% K1    - (N x l x m)         = k1i(t,s1)     = dy(t)/dui(t - s1)
% K2    - (N x N x l x m x m) = k2ij(t,s1,s2) = d2y(t)/dui(t - s1)duj(t - s2)
%
% System identification     - Bilinear approximation
%--------------------------------------------------------------------------
% M0    - (n x n)           df/dq
% M1    - {m}(n x n)        d2f/dqdu
% L1    - (l x n)           dg/dq
% L2    - {l}(n x n)        d2g/dqdq
%
%__________________________________________________________________________
%
% Returns the moments of the posterior p.d.f. of the parameters of a 
% nonlinear MIMO model under Gaussian assumptions
%
%              dx/dt  = f(x,u,P)
%                y    = g(x,u,P) + e                               (1)
%
% evaluated at x(0) = x0, using a Bayesian estimation scheme with priors
% on the model parameters P, specified in terms of expectations and 
% covariance. The estimation uses a Gauss-Newton method with MAP point 
% estimators at each iteration.  Both Volterra kernel and state-space 
% representations of the Bilinear approximation are provided.
% The Bilinear approximation to (1), evaluated at x(0) = x and u = 0 is:
%
%       dq/dt = M0*q + u(1)*M1{1}*q + u(2)*M1{2}*q + ....
%        y(i) = L1(i,:)*q + q'*L2{i}*q;
%
% where the states are augmented with a constant
%
%        q(t) = [1; x(t) - x(0)]
%
% The associated kernels are derived using closed form expressions based
% on the bilinear approximation.
%
%--------------------------------------------------------------------------
% If the inputs U and outputs Y are not specified the model is simply
% characterised in terms of its Volterra kernels and Bilinear
% approximation expanding around M.pE
%
% see also
% spm_nlsi_GN:   Bayesian parameter estimation using an EM/Gauss-Newton method
% spm_bireduce:  Reduction of a fully nonlinear MIMO system to Bilinear form
% spm_kernels:   Returns global Volterra kernels for a MIMO Bilinear system
%
% SEE NOTES AT THE END OF THIS SCRIPT FOR EXAMPLES
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_nlsi.m 3764 2010-03-08 20:18:10Z guillaume $

% check integrator
%--------------------------------------------------------------------------
try
    M.IS;
catch
    M.IS = 'spm_int';
end

% Expansion point (in parameter space) for Bilinear-kernel representations
%--------------------------------------------------------------------------
if nargin == 3

    % Gauss-Newton/Bayesian/EM estimation
    %======================================================================
    [Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y);

    if nargout < 4, varargout = {Ep,Cp,Eh}; return, end

else
    % Use prior expectation to expand around
    %----------------------------------------------------------------------
    Ep         = M.pE;
    
end


% Bilinear representation
%==========================================================================
[M0,M1,L1,L2] = spm_bireduce(M,Ep);


% Volterra kernels
%==========================================================================

% time bins (if not specified)
%--------------------------------------------------------------------------
try 
    dt   = M.dt;
    N    = M.N;
catch
    s    = real(eig(full(M0)));
    s    = max(s(s < 0));
    N    = 32;
    dt   = -4/(s*N);
    M.dt = dt;
    M.N  = N;
end

% get kernels
%--------------------------------------------------------------------------
[K0,K1,K2]   = spm_kernels(M0,M1,L1,L2,N,dt);

% graphics
%==========================================================================
if ~isdeployed && length(dbstack) < 2

    subplot(2,1,1)
    plot([1:N]*dt,K1(:,:,1))
    xlabel('time')
    ylabel('response')
    title('1st-order kernel')
    grid on

    subplot(2,1,2)
    imagesc([1:N]*dt,[1:N]*dt,K2(:,:,1,1,1))
    xlabel('time')
    ylabel('time')
    title('2nd-order kernel')
    grid on
    axis image
    drawnow
end


% output arguments
%--------------------------------------------------------------------------
if nargin == 3
    varargout = {Ep,Cp,Eh,K0,K1,K2,M0,M1,L1,L2,F};
else
    varargout = {K0,K1,K2,M0,M1,L1,L2};
end


return


% NOTES ON USE
%==========================================================================
% Consider the system
%
%              dx/dt  = 1./(1 + exp(-x*P)) + u
%                y    = x + e
%
% Specify a model structure:
%--------------------------------------------------------------------------
M.f  = inline('1./(1 + exp(-P*x)) + [u; 0]','x','u','P','M');
M.g  = inline('x','x','u','P','M');
M.pE = [-1 .3;.5 -1];           % Prior expectation of parameters
M.pC = speye(4,4);              % Prior covariance for parameters
M.x  = zeros(2,1)               % intial state x(0)
M.m  = 1;                       % number of inputs
M.n  = 2;                       % number of states
M.l  = 2;                       % number of outputs

% characterise M in terms of Volterra kernels and Bilinear matrices:
%--------------------------------------------------------------------------
[K0,K1,K2,M0,M1,L1,L2] = spm_nlsi(M);


% or estimate the model parameters with inputs and outputs
%==========================================================================

% inputs
%--------------------------------------------------------------------------
U.name = 'input';
U.u    = randn(64,1);
U.dt   = 1;

% outputs
%--------------------------------------------------------------------------
Y.name = 'response';
y      = spm_int_D(M.pE,M,U);
Y.y    = y + randn(size(y))/32;
Y.dt   = U.dt*length(U.u)/length(Y.y);

% estimate
%--------------------------------------------------------------------------
[Ep,Cp,Eh,K0,K1,K2,M0,M1,L1,L2,F] = spm_nlsi(M,U,Y);