code clean-up and STDP logic fixes

Author: Vanessa Arndorfer

Simulator/Edges/Neuro/AllDynamicSTDPSynapses.cpp

@@ -17,7 +17,7 @@ AllDynamicSTDPSynapses::AllDynamicSTDPSynapses(int numVertices, int maxEdges) :
 ///  Setup the internal structure of the class (allocate memories and initialize them).
 void AllDynamicSTDPSynapses::setupEdges()
 {
-   setupEdges(Simulator::getInstance().getDeltaT(),
+   setupEdges(Simulator::getInstance().getTotalVertices(),
               Simulator::getInstance().getMaxEdgesPerVertex());
 }
 

Simulator/Edges/Neuro/AllDynamicSTDPSynapses.h

@@ -9,7 +9,7 @@
  *  Each kind of synapse parameter is stored in a 2D array. Each item in the first
  *  dimension of the array corresponds with each neuron, and each item in the second
  *  dimension of the array corresponds with a synapse parameter of each synapse of the neuron.
- * Because each neuron owns different number of synapses, the number of synapses
+ *  Because each neuron owns different number of synapses, the number of synapses
  *  for each neuron is stored in a 1D array, edge_counts.
  *
  *  For CUDA implementation, we used another structure, AllDSSynapsesDevice, where synapse

Simulator/Edges/Neuro/AllSTDPSynapses.cpp

@@ -275,28 +275,28 @@ void AllSTDPSynapses::advanceEdge(BGSIZE iEdg, AllVertices &neurons)
       // pre and post neurons index
       int idxPre = sourceVertexIndex_[iEdg];
       int idxPost = destVertexIndex_[iEdg];
-      uint64_t spikeHistory, spikeHistory2;
+      uint64_t t_post, t_pre;
       BGFLOAT delta;
-      BGFLOAT epre, epost;
+
+      // epre and epost are set to 1 for the independent model [Froemke and Dan (2002)]
+      BGFLOAT epre = 1.0;
+      BGFLOAT epost = 1.0;
 
       if (fPre) {   // preSpikeHit
-         // spikeCount points to the next available position of spike_history,
-         // so the getSpikeHistory w/offset = -2 will return the spike time
-         // just one before the last spike.
-         spikeHistory = spNeurons.getSpikeHistory(idxPre, -2);
-
-         epre = 1.0;
-         epost = 1.0;
-         // call the learning function stdpLearning() for each pair of
-         // pre-post spikes
+         // call the learning function stdpLearning() for each pair of pre-post spikes
          int offIndex = -1;   // last spike
          while (true) {
-            spikeHistory = spNeurons.getSpikeHistory(idxPost, offIndex);
-            if (spikeHistory == numeric_limits<unsigned long>::max())
+            // time of the offset post-spike hit
+            uint64_t t_post = spNeurons.getSpikeHistory(idxPost, offIndex);
+
+            // break if no post-spike is found
+            if (t_post == numeric_limits<unsigned long>::max())
                break;
+
             // delta is the spike interval between pre-post spikes
-            // (include pre-synaptic transmission delay)
-            delta = -static_cast<BGFLOAT>(g_simulationStep - spikeHistory) * deltaT;
+            // deltaT converts the timestep to seconds
+            delta = -static_cast<BGFLOAT>(g_simulationStep - t_post) * deltaT;
+
             /*
              LOG4CPLUS_DEBUG(fileLogger_,"\nAllSTDPSynapses::advanceSynapse: fPre" << endl
              << "\tiEdg: " << iEdg << endl
@@ -320,28 +320,25 @@ void AllSTDPSynapses::advanceEdge(BGSIZE iEdg, AllVertices &neurons)
       }
 
       if (fPost) {   // postSpikeHit
-                     // spikeCount points to the next available position of spike_history,
-                     // so the getSpikeHistory w/offset = -2 will return the spike time
-                     // just one before the last spike.
-         spikeHistory = spNeurons.getSpikeHistory(idxPost, -2);
-         epost = 1.0;
-         epre = 1;
-
-
-         // call the learning function stdpLearning() for each pair of
-         // post-pre spikes
+         // call the learning function stdpLearning() for each pair of post-pre spikes
          int offIndex = -1;   // last spike
          while (true) {
-            spikeHistory = spNeurons.getSpikeHistory(idxPre, offIndex);
-            if (spikeHistory == numeric_limits<unsigned long>::max())
+            // time when the pre-spike left the source neuron
+            t_pre = spNeurons.getSpikeHistory(idxPre, offIndex);
+
+            // break if no pre-spike is found
+            if (t_pre == numeric_limits<unsigned long>::max())
                break;
 
-            if (spikeHistory + total_delay > g_simulationStep) {
+            // delay accounts for the time it takes the spike to reach the destination neuron
+            if (t_pre + total_delay > g_simulationStep) {
                --offIndex;
                continue;
             }
+
             // delta is the spike interval between post-pre spikes
-            delta = static_cast<BGFLOAT>(g_simulationStep - spikeHistory - total_delay) * deltaT;
+            delta = static_cast<BGFLOAT>(g_simulationStep - t_pre - total_delay) * deltaT;
+
             /*
              LOG4CPLUS_DEBUG(fileLogger_,"\nAllSTDPSynapses::advanceSynapse: fPost" << endl
              << "\tiEdg: " << iEdg << endl
@@ -353,6 +350,7 @@ void AllSTDPSynapses::advanceEdge(BGSIZE iEdg, AllVertices &neurons)
              << "\tepost: " << epost << endl
              << "\tdelta: " << delta << endl << endl);
              */
+
             if (delta >= 3.0 * taupos)
                break;
 
@@ -366,7 +364,6 @@ void AllSTDPSynapses::advanceEdge(BGSIZE iEdg, AllVertices &neurons)
    psr *= decay;
 }
 
-
 BGFLOAT AllSTDPSynapses::synapticWeightModification(BGSIZE iEdg, BGFLOAT synapticWeight,
                                                     double delta)
 {
@@ -379,15 +376,11 @@ BGFLOAT AllSTDPSynapses::synapticWeightModification(BGSIZE iEdg, BGFLOAT synapti
    BGFLOAT Apos = Apos_[iEdg];
    BGFLOAT Wex = Wex_[iEdg];
    BGFLOAT &W = W_[iEdg];
-   edgeType type = type_[iEdg];
    BGFLOAT dw = 0;
-   BGFLOAT oldW = W;
-
-   // BGFLOAT modDelta = fabs(delta);
 
+   // muneg and mupos default to 0 for the basic multiplicative model
    if (delta < -STDPgap) {
       // depression
-
       dw = pow(fabs(W) / Wex, muneg) * Aneg * exp(delta / tauneg);   // normalize
    } else if (delta > STDPgap) {
       // potentiation
@@ -411,24 +404,17 @@ void AllSTDPSynapses::stdpLearning(BGSIZE iEdg, double delta, double epost, doub
                                    int srcVertex, int destVertex)
 {
    BGFLOAT STDPgap = STDPgap_[iEdg];
-   BGFLOAT muneg = muneg_[iEdg];
-   BGFLOAT mupos = mupos_[iEdg];
-   BGFLOAT tauneg = tauneg_[iEdg];
-   BGFLOAT taupos = taupos_[iEdg];
-   BGFLOAT Aneg = Aneg_[iEdg];
-   BGFLOAT Apos = Apos_[iEdg];
    BGFLOAT Wex = Wex_[iEdg];
    BGFLOAT &W = W_[iEdg];
    edgeType type = type_[iEdg];
    BGFLOAT oldW = W;
-   // BGFLOAT modDelta = fabs(delta);
 
-   if (delta <= fabs(STDPgap)) {
+   if (fabs(delta) <= STDPgap) {
       return;
    }
 
    // dw is the fractional change in synaptic strength; add 1.0 to become the scaling ratio
-   //dw = 1.0 + dw * epre * epost;
+   // dw = 1.0 + dw * epre * epost; // used for the non-independent model
    BGFLOAT dw = 1.0 + synapticWeightModification(iEdg, W, delta);
 
    // if scaling ratio is less than zero, set it to zero so this synapse, its
@@ -443,6 +429,7 @@ void AllSTDPSynapses::stdpLearning(BGSIZE iEdg, double delta, double epost, doub
    if (fabs(W) > Wex) {
       W = edgSign(type) * Wex;
    }
+   
    /*
     LOG4CPLUS_DEBUG(edgeLogger_, endl <<
     "iEdg value " << iEdg

Simulator/Edges/Neuro/AllSTDPSynapses.h

@@ -9,7 +9,7 @@
  *  Each kind of synapse parameter is stored in a 2D array. Each item in the first
  *  dimension of the array corresponds with each neuron, and each item in the second
  *  dimension of the array corresponds with a synapse parameter of each synapse of the neuron.
- * Because each neuron owns different number of synapses, the number of synapses
+ *  Because each neuron owns different number of synapses, the number of synapses
  *  for each neuron is stored in a 1D array, edge_counts.
  *
  *  For CUDA implementation, we used another structure, AllDSSynapsesDevice, where synapse

configfiles/stdp_fE_0.90_10000.xml

@@ -3,8 +3,8 @@
     <SimInfoParams name="SimInfoParams">
         <graphmlFile name="graphmlFile">../configfiles/graphs/fE_0.90_10000.graphml</graphmlFile>
         <SimParams name="SimParams">
-          <epochDuration name="epochDuration">100.0</epochDuration>
-          <numEpochs name="numEpochs">3</numEpochs>
+          <epochDuration name="epochDuration">10.0</epochDuration>
+          <numEpochs name="numEpochs">1</numEpochs>
         </SimParams>
         <SimConfig name="SimConfig">
           <maxFiringRate name="maxFiringRate">200</maxFiringRate>
@@ -106,7 +106,7 @@
           <e name="e">5.0265e-7</e>
         </Wex> 
         <Aneg name="Aneg">
-          <i name="i">-52e-2</i>
+          <i name="i">-0.52</i>
           <e name="e">-0.52</e>
         </Aneg>
          <Apos name="Apos">
@@ -116,17 +116,6 @@
       </EdgesParams>
 
       <ConnectionsParams class="ConnStatic" name="ConnectionsParams">
-        <threshConnsRadius name="threshConnsRadius">0</threshConnsRadius>
-        <connsPerNeuron name="connsPerNeuron">0</connsPerNeuron>
-        <rewiringProbability name="rewiringProbability">0</rewiringProbability>
-        <excWeight name=" excWeight">
-          <min name="min">2.36936e-38</min>
-          <max name="max">2.35106e-38</max>
-        </excWeight>
-        <inhWeight name=" inhWeight">
-          <min name="min">9.18355e-41</min>
-          <max name="max">3.58732e-43</max>
-        </inhWeight>
       </ConnectionsParams>
 
       <LayoutParams class="LayoutNeuro" name="LayoutParams">