Class DynamicStdpSynapse

                                   object --+                                    
                                            |                                    
                                  ??.instance --+                                
                                                |                                
                                 SimObjectFactory --+                            
                                                    |                            
                                            SimObject --+                        
                                                        |                        
                                      FiniteSpikeResponse --+                    
                                                            |                    
                                ExponentialDecaySpikeResponse --+                
                                                                |                
                                         CuBaSyn_ExponentialDecay --+            
                                                                    |            
                                  DynamicSyn_CuBaSyn_ExponentialDecay --+        
                                                                        |        
                  EachPairStdpSynapse_DynamicSyn_CuBaSyn_ExponentialDecay --+    
                                                                            |    
FroemkeDanStdpSynapse_EachPairStdpSynapse_DynamicSyn_CuBaSyn_ExponentialDecay --+
                                                                                |
                                                                               DynamicStdpSynapse

Help on DynamicStdpSynapse [class]:
Dynamic synapse implementing STDP plasticity. 

 STDP is due to Froemke and Dan Nature 416 (32002). 
 Implements the basic weight update for a time difference \f$Delta = 
 t_{post}-t_{pre}\f$ with presynaptic spike at time \f$t_{pre}\f$ and 
 postsynaptic spike at time \f$t_{post}\f$. Then, the weight update is given by 
 \f$dw = Apos  exp(-Deltataupos)\f$ for \f$Delta > 0\f$, and \f$dw = Aneg  
 exp(-Deltatauneg)\f$ for \f$Delta < 0\f$. (set \f$useFroemkeDanSTDP=0\f$ and 
 \f$mupos=muneg=0\f$ for this basic update rule). 
 
 It is also possible to use an 
 extended multiplicative update by changing mupos and muneg. Then \f$dw = 
 (Wex-W)^{mupos}  Apos  exp(-Deltataupos)\f$ for \f$Delta > 0\f$ and \f$dw = 
 W^{mupos}  Aneg  exp(Deltatauneg)\f$ for \f$Delta < 0\f$. (see Guetig, 
 Aharonov, Rotter and Sompolinsky (2003). Learning input correlations through 
 non-linear asymmetric Hebbian plasticity. Journal of Neuroscience 23. 
 pp.3697-3714.) 
 
 Set \f$useFroemkeDanSTDP=1\f$ (this is the default value) and 
 use \f$tauspost\f$ and \f$tauspre\f$ for the rule given in Froemke and Dan 
 (2002). Spike-timing-dependent synaptic modification induced by natural spike 
 trains. Nature 416 (32002).