class WeightUpdateModels::StaticPulseDendriticDelay¶
Overview¶
Pulse-coupled, static synapse with heterogenous dendritic delays. More…
#include <weightUpdateModels.h> class StaticPulseDendriticDelay: public WeightUpdateModels::Base { public: // typedefs typedef Snippet::ValueBase<0> ParamValues; typedef Models::VarInitContainerBase<2> VarValues; typedef Models::VarInitContainerBase<0> PreVarValues; typedef Models::VarInitContainerBase<0> PostVarValues; // methods static const StaticPulseDendriticDelay* getInstance(); virtual VarVec getVars() const; virtual std::string getSimCode() const; };
Inherited Members¶
public: // typedefs typedef std::vector<std::string> StringVec; typedef std::vector<EGP> EGPVec; typedef std::vector<ParamVal> ParamValVec; typedef std::vector<DerivedParam> DerivedParamVec; typedef std::vector<Var> VarVec; // structs struct DerivedParam; struct EGP; struct ParamVal; struct Var; // methods virtual ~Base(); virtual StringVec getParamNames() const; virtual DerivedParamVec getDerivedParams() const; virtual VarVec getVars() const; virtual EGPVec getExtraGlobalParams() const; size_t getVarIndex(const std::string& varName) const; size_t getExtraGlobalParamIndex(const std::string& paramName) const; virtual std::string getSimCode() const; virtual std::string getEventCode() const; virtual std::string getLearnPostCode() const; virtual std::string getSynapseDynamicsCode() const; virtual std::string getEventThresholdConditionCode() const; virtual std::string getSimSupportCode() const; virtual std::string getLearnPostSupportCode() const; virtual std::string getSynapseDynamicsSuppportCode() const; virtual std::string getPreSpikeCode() const; virtual std::string getPostSpikeCode() const; virtual VarVec getPreVars() const; virtual VarVec getPostVars() const; virtual bool isPreSpikeTimeRequired() const; virtual bool isPostSpikeTimeRequired() const; size_t getPreVarIndex(const std::string& varName) const; size_t getPostVarIndex(const std::string& varName) const;
Detailed Documentation¶
Pulse-coupled, static synapse with heterogenous dendritic delays.
No learning rule is applied to the synapse and for each pre-synaptic spikes, the synaptic conductances are simply added to the postsynaptic input variable. The model has 2 variables:
- g - conductance of scalar type
- d - dendritic delay in timesteps and no other parameters.
sim code is:
" $(addToInSynDelay, $(g), $(d));\n\