function data = sim4_step(data, pars)
EPSP=zeros(pars.N,1);
%
% I> J
%
for j=1:pars.N,
for k=data.I_act,
%
% only add connected neurons
%
if (data.C(k,j)),
EPSP(j) = EPSP(j) + data.I(k)*data.W(k,j);
end;
end;
end;
%
% J> J
%
for j=1:pars.N,
for k=data.RL_act,
%
% only add connected neurons
%
if (data.C_RL(k,j)),
EPSP(j) = EPSP(j) + data.out_J(k)*data.W_RL(k,j);
end;
end;
end;
data.out_EPSP = EPSP;
% prepare IPSC as inputdriven and Gauss background
if (pars.H.strength > 0),
if (strcmp(pars.H.type,'normal')),
data.IPSP= pars.H.mu + pars.H.sigma*data.rnd.NR;
% data.IPSP= pars.H.mu + pars.H.sigma*randn(pars.N,1);
else
data.IPSP=lognrnd(pars.H.mu,sqrt(pars.H.s2),pars.N,1);
end;
else
data.IPSP=zeros(pars.N,1);
end;
IPSP_step=data.IPSP;
%
% EPSP  IPSP no negative rates
%
for j=1:pars.N,
if (EPSP(j) > IPSP_step(j)),
EPSP(j) = EPSP(j) IPSP_step(j);
else
EPSP(j) = 1e30;
end;
end;
% convert Hz > mV
EPSP = pars.f_mV*EPSP;
% using: 0.3mV = 40pA
EPSC = EPSP *0.04/0.3; % into nA
data.out_input_pA = EPSC;
%
% go through neurons
%
outJ=zeros(pars.N,1);
for j=1:pars.N,
outJ(j) = data.G(j)*EPSC(j);
end;
data.out_J = outJ;
