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: demo_VolterraKernels.m
    path of file: ./repos/VBA-toolbox/demos/1_advanced
    
    the code of the file until where you have to start completion: function [posterior, out] = demo_VolterraKernels ()
% // VBA toolbox //////////////////////////////////////////////////////////
%
% demo_VolterraKernels ()
% demo of Volterra kernel estimation and use
%
% This code demonstrates how to extract Volterra kernels from a
% dynamical system trajectory. In this particular example, 
% the dynamical system is in fact a Rescorla-Wagner learning agent. Here,
% we use Volterra kernel decomposition to evaluate the long term influence
% of an input (feedback) on the immediate and future responses.
%
% See also demo_dynLearningRate
%
% /////////////////////////////////////////////////////////////////////////

%% Global values
% =========================================================================
N = 1e3;

%% Definition of the model
% =========================================================================
% simple Rescorla-Wagner model
f_fname = @f_rwl;
g_fname = @g_Id;

%% Simulation
% =========================================================================
% parameters
x0 = nan; % random initial state
theta = 0.2; % learning rate
phi = [];
alpha = Inf;
sigma = 1e2;

% options
options = struct ();

% feedback: random positive of negative feedbacks
fb.h_fname = @h_Id; 
fb.inH.u = 2 * VBA_random ('Bernoulli', 0.5, 1, N) - 1;
fb.indy = [];
fb.indfb = 1; % where to store feedbacks in u

    
function [posterior, out] = demo_VolterraKernels ()
% // VBA toolbox //////////////////////////////////////////////////////////
%
% demo_VolterraKernels ()
% demo of Volterra kernel estimation and use
%
% This code demonstrates how to extract Volterra kernels from a
% dynamical system trajectory. In this particular example, 
% the dynamical system is in fact a Rescorla-Wagner learning agent. Here,
% we use Volterra kernel decomposition to evaluate the long term influence
% of an input (feedback) on the immediate and future responses.
%
% See also demo_dynLearningRate
%
% /////////////////////////////////////////////////////////////////////////

%% Global values
% =========================================================================
N = 1e3;

%% Definition of the model
% =========================================================================
% simple Rescorla-Wagner model
f_fname = @f_rwl;
g_fname = @g_Id;

%% Simulation
% =========================================================================
% parameters
x0 = nan; % random initial state
theta = 0.2; % learning rate
phi = [];
alpha = Inf;
sigma = 1e2;

% options
options = struct ();

% feedback: random positive of negative feedbacks
fb.h_fname = @h_Id; 
fb.inH.u = 2 * VBA_random ('Bernoulli', 0.5, 1, N) - 1;
fb.indy = [];
fb.indfb = 1; % where to store feedbacks in u

% inputs will be fed in by the feedback
u = nan(1, N);

% simulation routine
[y,x,x0,eta,e,u] = VBA_simulate (N,f_fname,g_fname,theta,phi,u,alpha,sigma,options,x0,fb);

%% Estimation
% =========================================================================
% dimensions of the problem
dim.n = 1;
dim.n_theta = 1;
dim.n_phi = 0;

% inversion routine
[posterior, out] = VBA_NLStateSpaceModel(y,u,f_fname,g_fname,dim,options);

%% Volterra analysis
% =========================================================================
% compute kernels (NB: you could also find them in out.diagnostics)
maxLag = 16;
kernels = VBA_getVolterraKernels (posterior, out, maxLag);

% compute influence of feedbacks on responses
theoretical = theta * (1 - theta) .^ (0 : maxLag);
empirical.mean = kernels.y.m';
empirical.var = kernels.y.v';

% display
f = VBA_figure('Name', 'Influence of feedback on fugzre responses');
plotUncertainTimeSeries(empirical.mean,sqrt(empirical.var),1, f);
hold on
plot(theoretical,'b')
legend