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#ifndef TRAINER_H
#define TRAINER_H
#include <stdio.h>
#include <pthread.h>
#include <map>
#include <queue>
#include "boost/tuple/tuple.hpp"
using namespace std;
class Trainer {
public:
FILE *fd_spike_in,
*fd_spike_out,
*fd_global_out,
*fd_global_in,
*fd_trace_out,
*fd_performance_out;
// init stuff
Trainer(int argc, char** argv);
void initConfig();
void initState();
void initGroups();
void initFiles();
void initThreads();
// main routine
void run();
// state vars
long currentTrial;
vector<int> groupFreq; // spikes fired during epoch for each symbol (=group of neurons)
vector< set<int>* > ioNeurons; // a set of neurons for each symbol to which the symbol is written and from wich it is read later
//queue< int > symbolHist; // stores the symbols displayed; written by thread_write, read by main thread
int currentSymbol;
double dopamin_level;
int state;
MS_Global msg;
// synchronisation vars
typedef boost::tuple<double, int, double> SpikeEvent; // <what time, wich neuron, current>
queue<SpikeEvent> incomingSpikes;
// TODO: outgoingSpikes;
pthread_mutex_t incomingSpikeLock, writerLock;
// TODO: , outgoingSpikeLock; (including a condition for the writer to wakeup upon if a previously empyt queue has been filled)
pthread_t thread_read, thread_write;
// configuration
// network
long neurons; // total number of neurons
long neuronsPerSymbol;
double noiseFreq; // of poisson noise (per neuron)
double noiseVoltage; // per noise spike
double reward; // per succesful trial
// learning task
double epochDuration; // a trial consists of several epochs
long numTrials;
int numSymbols; // number of different things to remember
//int readoutDelay; // number of epochs between symbol-write and symbol-read-epoch
int refractoryPeriods; // how many epochs to wait after a trial finished until we start with a new trial
};
// seperate thread to read all spikes neccessary because reading and
// writing to these descriptors could block and thus cause a deadlock
void *read_spikes(Trainer *t);
void *write_spikes(Trainer *t);
#endif // TRAINER_H
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