dlanguage.cpp

a useful spiking neural networks simulator

						nmap[s] = neuinfo(layername, i, &inhibitor);
					else if( layername == "input_layer" )
						nmap[s] = neuinfo(layername, i, (neuin[i % neuin.size()]));
					else if( layername == "output_layer" )
						nmap[s] = neuinfo(layername, i, (neuout[i % neuout.size()]));
					else if( layername == "hidden_layer" )
					{
						ostringstream oss; oss << i;
						tneuron *neu = new tneuron((synch ? "shidden(" : "dhidden(") + oss.str() + ")", 
							synch ? sgate_sig_hv_period : dgate_sig_hv_period, 
							synch ? sgate_thr_hv_period : dgate_thr_hv_period, 
							a1_offset, a1_offset+(synch ? sgate_hidden_thr_raise : dgate_hidden_thr_raise) );
						nmap[s] = neuinfo(layername, i, neu);
						neuhid.push_back(neu);
					}
					else
					{
						cout << "Unknown layer name " << layername << endl;
						abort();
					}
				}
				ifs >> s;
			}
			else if( s == "connection" )
			{
				string dstname;
				int nconn;
				ifs >> dstname >> nconn;
				if( nmap.find(dstname) == nmap.end() ) { cout << "unknown neuron " << dstname << endl; abort();}
				if( debug_readfile ) cout << "connection " << nconn << endl;
				tneuron_base *dest = nmap[dstname].ref;
				for( int i=0; i<nconn; i++ )
				{
					string srcname;
					real weight;
					int idelay;
					ifs >> srcname >> weight >> idelay;
					if( nmap.find(srcname)== nmap.end()  ) { cout << "unknown neuron " << srcname << endl; abort();}
					tneuron_base *src = nmap[srcname].ref;
					weight *= weight_scale;
					if( nmap[srcname].layername == "a1_layer" ) 
					{
						tsynapse *p1, *p2, *p3 = NULL;
						real delay = delay1 - inhibitor_delay;
						if( nmap[dstname].layername == "hidden_layer" )
							delay += -hidden_offset;
						weight += a1_offset;

						real inhibitor_fix = 0.0;
						if( ! synch )
						{
							weight -= a1_offset;
							incoindet.addSynapse(p3 = new tsynapse(*dest, delay - 0.00002 - incoindet_delay - coindet_offset, ((weight < 0 ? weight : 0) - inhibitor_suppress_weight)) );
//							inhibitor.addSynapse(p3 = new tsynapse(*dest, delay + 0.003 - 0.00001 - incoindet_delay , 0.3)) ;
//							inhibitor.addSynapse(p2 = new tsynapse(*dest, delay + coindet_duration_period, 1.0 ));
							inhibitor_fix = ((weight < 0 ? weight : 0) - inhibitor_suppress_weight) * pow(decay_level, (delayed_gate_gap + coindet_offset) / coindet_duration_period );
//							cout << nmap[dstname].layername << "(" << nmap[dstname].nneuron << ") weight=" << weight << ", level=" << decay_level << ", pow=" << pow(decay_level, (delayed_gate_gap + coindet_offset) / coindet_duration_period ) << ", fix=" << inhibitor_fix << endl;
							weight += a1_offset;
						}
						coindet.addSynapse(p1 = new tsynapse(*dest, 
								delay + (! synch ? -coindet_duration_period : - 0.00001), 
								((weight < 0 ? weight : 0) - coindet_suppress_weight)/sqrt(decay_level)));
						coindet.addSynapse(p2 = new tsynapse(*dest, 
								delay, 
								((weight > 0 ? weight : 0) + coindet_suppress_weight)*sqrt(decay_level) - inhibitor_fix - (!synch ? delay_weight_decrease : synch_weight_decrease)));
//						if( nmap[dstname].layername == "output_layer" && 
//						(   nmap[dstname].nneuron == str_to_wordid["the"] || nmap[dstname].nneuron == NWORDS + pos_det 
//						 || nmap[dstname].nneuron == str_to_wordid["girl"] || nmap[dstname].nneuron == NWORDS + pos_n ))
//						 {
//						 	cout << "debug synapse " << nmap[srcname].nneuron << " -> " << nmap[dstname].nneuron << endl;
//							p1->setDeb(true);
//							p2->setDeb(true);
//							if( p3 )
//								p3->setDeb(true);
//						 }
					}
					else
					{
						real delay = (nmap[srcname].layername == "input_layer" && nmap[srcname].nneuron >= neuin.size()) ? delay1 + delay_input_gap : delay1;
						if( nmap[srcname].layername == "hidden_layer" )
							delay = hidden_offset;
						else if( nmap[dstname].layername == "hidden_layer" )
							delay -= hidden_offset;
						tsynapse *p;
						if( weight < 0.0 )
						{
							delay -= synch ? synch_minus_preceding : delay_minus_preceding;
							if( !synch )
								weight /= pow(decay_level, delay_minus_preceding / coindet_duration_period );
						}
						src->addSynapse(p = new tsynapse(*dest, delay - (! synch ? coindet_offset : 0), weight));
//						if( nmap[dstname].nneuron == str_to_wordid["the"] || nmap[dstname].nneuron == NWORDS + pos_det 
//						 || nmap[dstname].nneuron == str_to_wordid["girl"] || nmap[dstname].nneuron == NWORDS + pos_n )
//						 {
//						 	cout << "debug synapse " << nmap[srcname].nneuron << " -> " << nmap[dstname].nneuron << endl;
//							p->setDeb(true);
//						 }
					}
				}
				ifs >> s;
			}
			else
				break;
		}
	}
}


int main(int argc, char **argv)
{
	cout << "Punnets language simulation program  (c) 2003 Makino Takaki." << endl;
	cout << "Punnets is free software, covered by the GNU General Public License." << endl;
	cout << endl;

	const char *datafilename = "lang.dat";
	const char *plotfilename = "lang.plt";

	const unsigned int nexts = NWORDS + NELEMS;
	vector<tneuron_ext *> exts(nexts);
	vector<tneuron *> hidden;
	vector<tneuron *> sgates(nexts);
	vector<tneuron *> dgates(nexts);
	tneuron inhibitor("inhibitor", 0.0005, 0.0005, 1.0, 128.0);
	tneuron coindet("coindet", 0.003, 0.05, 1.0, 5.0);
	tneuron incoindet("incoindet", 0.003, 0.003, 1.0, 2.0);

	inhibitor.addSynapse(new tsynapse(coindet, inhibitor_delay, 0.67));
	inhibitor.addSynapse(new tsynapse(coindet, inhibitor_delay + delayed_gate_gap, 0.67));
	inhibitor.addSynapse(new tsynapse(incoindet, incoindet_delay, 1.2));
	inhibitor.addSynapse(new tsynapse(incoindet, incoindet_delay + delayed_gate_gap, 1.2));
	coindet.addSynapse(new tsynapse(incoindet, incoindet_delay - inhibitor_delay - 0.0015, -4));

	for( unsigned int i=0; i<NWORDS; i++ )
		str_to_wordid[ words[i].word ] = i;
	

	tscheduler scheduler;

	ofstream ofs(datafilename);
	cout << "Produce simulation log file '" << datafilename << "'." << endl;
	tlogger logger(ofs, 0.001, 0.01, test_until);
//	tlogger logger(ofs, 0.00001, 0.609,0.621);

	for( unsigned int i=0; i<nexts; i++ )
	{
		ostringstream oss;
		exts[i] = new tneuron_ext("repre'" + (i < NWORDS ? words[i].word : posstrs[i-NWORDS]) + "'", 0.0045);
		sgates[i] = new tneuron("sgate'" + (i < NWORDS ? words[i].word : posstrs[i-NWORDS]) + "'", 
				sgate_sig_hv_period, sgate_thr_hv_period, a1_offset, a1_offset+64 );
		dgates[i] = new tneuron("dgate'" + (i < NWORDS ? words[i].word : posstrs[i-NWORDS]) + "'", 
				dgate_sig_hv_period, dgate_thr_hv_period, a1_offset, a1_offset+64 );

//if(! test_applier) {
//		exts[i]->addExt(new func_const(-1.3));
//		exts[i]->addExt(new func_sine(1.0 /* width */, 8 * 2*M_PI /* angle velo */, 0.0 /* phase */ ));
//		exts[i]->addExt(new func_sineshot_int(1 /* width */, 1.6 * 2*M_PI /* angle velo */ ));
//		inhibitor.addSynapse(new tsynapse_addfunc(*exts[i], 0.0001, new func_sineshot(-0.7, (1/0.027) * 2*M_PI, -1.0), -0.8));
//}

		exts[i]->addExt(new func_const(-1.4));
if( ! test_applier ) {
		exts[i]->addExt(new func_sine(1.0 /* width */, 8 * 2*M_PI /* angle velo */, 0.0 /* phase */ ));
		exts[i]->addExt(new func_sineshot_int(1.27 /* width */, 1.59 * 2*M_PI /* angle velo */ ));
		inhibitor.addSynapse(new tsynapse_addfunc(*exts[i], 0.0001, new func_sineshot(-0.7, (1/0.021) * 2*M_PI, -1.0), new func_sineshot::message_set_t0, -0.25));
}
		exts[i]->addSynapse(new tsynapse(inhibitor, inhibitor_delay, 1.5));
		exts[i]->addSynapse(new tsynapse(*exts[i], inhibitor_delay, -4.0));
		exts[i]->setLoopBack(scheduler, 0.0);
if( ! test_applier ) {
		sgates[i]->addSynapse(new tsynapse(*exts[i], gates_delay, 4.0));
		dgates[i]->addSynapse(new tsynapse(*exts[i], gates_delay, 5.5));
}

		if( 
			i == str_to_wordid["the"] || i == NWORDS + pos_det 
		||  i == str_to_wordid["Mary"] || i == NWORDS + pos_pn3
		||  i == str_to_wordid["girl"] || i == NWORDS + pos_n
		||  i == str_to_wordid["loves"] || i == NWORDS + pos_vt
		||  i == str_to_wordid["(lover)"] || i == NWORDS + pos_vt_subj
		 	)
		{
			real off =
			           (i == str_to_wordid["Mary"] || i == NWORDS + pos_pn3) ? 40 : 
			           (i == str_to_wordid["(lover)"] || i == NWORDS + pos_vt_subj) ? 30 : 
			           (i == str_to_wordid["loves"] || i == NWORDS + pos_vt) ? 20 : 
			           (i == str_to_wordid["the"] || i == NWORDS + pos_det) ? 10 : 0;
			off += (i >= NWORDS) ? 4 : 0;
			logger.add(*exts[i], 0, false, false, off * 3.5 / 5);
			// logger.add(*sgates[i], 0, false, false, off);	
			// logger.add(*dgates[i], 0, false, false, 1.0 + off);
//			exts[i]->setDeb(true);
//			sgates[i]->setDeb(true);
//			dgates[i]->setDeb(true);
		}
	}

//	logger.add( inhibitor, 0, false, false, -8.0 );

	read_file(exts, hidden, sgates, inhibitor, inhibitor, "synch.dat", synchro_gate_delay, 0.0             , 
		1.0 , synchro_hidden_offset, true);
	read_file(exts, hidden, dgates, incoindet, coindet  , "delay.dat", delayed_gate_delay, delayed_gate_gap, 
		exp(-(M_LN2 / dgate_sig_hv_period)*(coindet_duration_period)) , delayed_hidden_offset, false );

	ofstream ofs2(plotfilename);
	cout << "Produce GNUPLOT plotting file '" << plotfilename << "'." << endl;
	logger.gnuplot_def(ofs2, datafilename);

	logger.schedule(scheduler);

	scheduler.scheduleEvent( 0.61, makePulse( * exts[ str_to_wordid[ "Mary" ] ], 5.0 ) );
	scheduler.scheduleEvent( 0.975, makePulse( * exts[ str_to_wordid[ "loves" ] ], 5.0 ) );
	scheduler.scheduleEvent( 1.725, makePulse( * exts[ str_to_wordid[ "the" ] ], 5.0 ) );
	scheduler.scheduleEvent( 2.225, makePulse( * exts[ str_to_wordid[ "girl" ] ], 5.0 ) );

	scheduler.run(test_until);

	cout << "Simulation finished." << endl;
	return 0;
}