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_fx_lfp.m
    path of file: ./repos/bspm/thirdparty/spm8/toolbox/Neural_Models
    
    the code of the file until where you have to start completion: function [f,J] = spm_fx_lfp(x,u,P,M)
% state equations for a neural mass model of erps
% FORMAT [f,J] = spm_fx_lfp(x,u,P,M)
% x      - state vector
%   x(:,1)  - voltage (spiny stellate cells)
%   x(:,2)  - voltage (pyramidal cells)         +ve
%   x(:,3)  - voltage (pyramidal cells)         -ve
%   x(:,4)  - current (spiny stellate cells)    +ve 
%   x(:,5)  - current (pyramidal cells)         +ve
%   x(:,6)  - current (pyramidal cells)         -ve
%   x(:,7)  - voltage (inhibitory interneurons) +ve
%   x(:,8)  - current (inhibitory interneurons) +ve
%   x(:,9)  - voltage (pyramidal cells)
%   x(:,10) - voltage (inhibitory interneurons) -ve

    
function [f,J] = spm_fx_lfp(x,u,P,M)
% state equations for a neural mass model of erps
% FORMAT [f,J] = spm_fx_lfp(x,u,P,M)
% x      - state vector
%   x(:,1)  - voltage (spiny stellate cells)
%   x(:,2)  - voltage (pyramidal cells)         +ve
%   x(:,3)  - voltage (pyramidal cells)         -ve
%   x(:,4)  - current (spiny stellate cells)    +ve 
%   x(:,5)  - current (pyramidal cells)         +ve
%   x(:,6)  - current (pyramidal cells)         -ve
%   x(:,7)  - voltage (inhibitory interneurons) +ve
%   x(:,8)  - current (inhibitory interneurons) +ve
%   x(:,9)  - voltage (pyramidal cells)
%   x(:,10) - voltage (inhibitory interneurons) -ve
%   x(:,11) - current (inhibitory interneurons) -ve
%   x(:,12) - voltage (inhibitory interneurons)
%
%   x(:,13) - slow potassium conductance
%
% f    = dx(t)/dt  = f(x(t))
% J    = df/dx
%
% Fixed parameter scaling [Defaults]
%
%  E = [32 16 4];             % extrinsic rates (forward, backward, lateral)
%  G = [1 1 1/2 1/2 1/8]*128; % intrinsic rates (g1, g2 g3, g4, g5)
%  D = [2 16];                % propagation delays (intrinsic, extrinsic)
%  H = [4 32];                % receptor densities (excitatory, inhibitory)
%  T = [4 16];                % synaptic constants (excitatory, inhibitory)
%  R = [2 1];                 % parameters of static nonlinearity
%
%__________________________________________________________________________
%
% This is a simplified version of spm_fx_erp
%
% David O, Friston KJ (2003) A neural mass model for MEG/EEG: coupling and
% neuronal dynamics. NeuroImage 20: 1743-1755
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_fx_lfp.m 4402 2011-07-21 12:37:24Z karl $

% check if intrinsic connections are free parameters
%--------------------------------------------------------------------------
try, P.G; catch, P.G = 0; end