%
% set up the sim data structure with default values
%
% $Revision:$
%
%
if (exist('T_sim')),
T_upd = T_sim;
else
T_upd = 1000;
T_sim = T_upd;
end;
if (~exist('N_nn')),
N_nn = 1;
end;
if (~exist('FN')),
FN='interactive';
end;
if (~exist('neuron_type')),
neuron_type = 'neuron_nmda3';
end;
FN_INP='dummy';
sim.N_nn = N_nn;
sim.exp = 'EXPERIMENT';
sim.input_units = 'current ';
sim.activity_thr =-0;
%sim.activity_win = 7;
% this enables to recognize freq > 100Hz, but might yield problems
% with low firing rates
sim.activity_win = 2;
sim.neuron = neuron_type;
sim.offset = 1;
sim.get_channels = 1;
sim.description = 'interactive experiment';
sim.T_upd = T_upd;
sim.ts = 1; % basic time unit = ms
sim.nA = 1/2.3;
sim.nA_units = 1;
sim.date=now;
sim.script='interactive.m';
sim.FN = FN;
sim.FN_INP = FN_INP;
%
% some control variables (most of them not used frequently)
% need to be in sim
%
sim.do_sim = 1;
sim.do_gen_inputs = 1;
sim.do_analyze_inp = 0;
sim.do_save_inputs = 0;
sim.do_table = 0;
sim.do_hold = 0;
sim.N_upd = 1; % number of update cycles
%-----------------------------------------------
path(path,'../neuron');
path(path,'../analysis');
path(path,'../input');
path(path,'../syn_response');
path(path,'../gain_filter');
path(path,'../interactive');
path(path,'../netsim');
[sim,dummy1, dummy, nn_mu_params] = eval(sprintf('init_%s(sim)',sim.neuron));
% definition of neuron mu parameters
%-----------------------------------------------
%
% individual parameters
%
% 1 K
% 2 CaL
% 3 KAs
% 4 Na
% 5 NaS
% 6 Kaf
% 7 Kir
% 8 AHP
% 9 M
% 10 mu_NMDA (for Cai)
% 11 mu_EBIO (Cai <-> SK)
% 12 NMDA strength (par(12)*I_nmda)
% 13 H
% 14 mu_AMPA (default = 1) injected into AMPA (=soma) = mu(14)*I_S(1,:)
% 15 mu_NMDA injected into NMDA = mu(15)*I_S(1,:)
%
offset = 100; % for plots
rand('seed',99);
randn('seed',1387);
init_input;
% defaults for run_sim_gain
exc_Mp = [500, 1000, 2000, 3000];
inh_Mn = [50, 100, 200, 300];
%
% display I_S_inj instead of I_S
%
sim_gain.disp_inp_inj = 1;