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_nested_oscillations_demo.m
    path of file: ./repos/spm/toolbox/Neural_Models
    
    the code of the file until where you have to start completion: function spm_nested_oscillations_demo
% Demo routine for neural mass models of nested oscillations
%==========================================================================
% 
% This demo simply illustrates nested oscillations in a three-subpopulation
% source that are caused by nonlinear interactions between voltage and
% conductance. Put simply, a slow sinusoidal drive elicits periods of bursting
% to produce phase-amplitude coupling in the ensuing dynamics.  We look at
% this using both neural-mass and mean-field models.  See Marreiros et al:
% 
% Population dynamics under the Laplace assumption.
% 
% A Marreiros, J Daunizeau, S Kiebel, L Harrison & Karl Friston
% 
% Abstract
% In this paper, we describe a generic approach to modelling dynamics in
% neuronal populations.  This approach retains a full density on the states
% of neuronal populations but resolves the problem of solving
% high-dimensional problems by re-formulating density dynamics in terms of
% ordinary differential equations on the sufficient statistics of the
% densities considered.  The particular form for the population density we
% adopt is a Gaussian density (c.f., a Laplace assumption). This means
% population dynamics are described completely by equations governing the
% evolution of the population's mean and covariance.  We derive these
% equations from the Fokker-Planck formalism and illustrate their
% application to a reasonably simple conductance-based model of neuronal
% exchanges.  One interesting aspect of this formulation is that we can
% uncouple the mean and covariance to furnish a neural-mass model, which

    
function spm_nested_oscillations_demo
% Demo routine for neural mass models of nested oscillations
%==========================================================================
% 
% This demo simply illustrates nested oscillations in a three-subpopulation
% source that are caused by nonlinear interactions between voltage and
% conductance. Put simply, a slow sinusoidal drive elicits periods of bursting
% to produce phase-amplitude coupling in the ensuing dynamics.  We look at
% this using both neural-mass and mean-field models.  See Marreiros et al:
% 
% Population dynamics under the Laplace assumption.
% 
% A Marreiros, J Daunizeau, S Kiebel, L Harrison & Karl Friston
% 
% Abstract
% In this paper, we describe a generic approach to modelling dynamics in
% neuronal populations.  This approach retains a full density on the states
% of neuronal populations but resolves the problem of solving
% high-dimensional problems by re-formulating density dynamics in terms of
% ordinary differential equations on the sufficient statistics of the
% densities considered.  The particular form for the population density we
% adopt is a Gaussian density (c.f., a Laplace assumption). This means
% population dynamics are described completely by equations governing the
% evolution of the population's mean and covariance.  We derive these
% equations from the Fokker-Planck formalism and illustrate their
% application to a reasonably simple conductance-based model of neuronal
% exchanges.  One interesting aspect of this formulation is that we can
% uncouple the mean and covariance to furnish a neural-mass model, which
% rests only on the populations mean.  This enables to compare equivalent
% mean-field and neural-mass models of the same populations and evaluate,
% quantitatively, the contribution of population variance to the expected
% dynamics.  The mean-field model presented here will form the basis of a
% dynamic causal model of observed electromagnetic signals in future work.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging
 
 
% number of regions in coupled map lattice
%--------------------------------------------------------------------------
n     = 1;
 
% extrinsic network connections
%--------------------------------------------------------------------------
if n > 1
A{1}  = diag(ones(n - 1,1),-1);
else
    A{1} = 0;
end