Weight update models¶

Currently 4 predefined weight update models are available:

For more details about these built-in synapse models, see Nowotny2010.

Defining a new weight update model¶

Like the neuron models discussed in Defining your own neuron type, new weight update models are created by defining a class. Weight update models should all be derived from WeightUpdateModel::Base and, for convenience, the methods a new weight update model should implement can be implemented using macros:

SET_DERIVED_PARAMS(), SET_PARAM_NAMES(), SET_VARS() and SET_EXTRA_GLOBAL_PARAMS() perform the same roles as they do in the neuron models discussed in Defining your own neuron type.
DECLARE_WEIGHT_UPDATE_MODEL(TYPE, NUM_PARAMS, NUM_VARS, NUM_PRE_VARS, NUM_POST_VARS) is an extended version of DECLARE_MODEL() which declares the boilerplate code required for a weight update model with pre and postsynaptic as well as per-synapse state variables.
SET_PRE_VARS() and SET_POST_VARS() define state variables associated with pre or postsynaptic neurons rather than synapses. These are typically used to efficiently implement trace variables for use in STDP learning rules Morrison2008. Like other state variables, variables defined here as NAME can be accessed in weight update model code strings using the $(NAME) syntax.
SET_SIM_CODE(SIM_CODE) : defines the simulation code that is used when a true spike is detected. The update is performed only in timesteps after a neuron in the presynaptic population has fulfilled its threshold detection condition. Typically, spikes lead to update of synaptic variables that then lead to the activation of input into the post-synaptic neuron. Most of the time these inputs add linearly at the post-synaptic neuron. This is assumed in GeNN and the term to be added to the activation of the post-synaptic neuron should be applied using the the $(addToInSyn, weight) function. For example
```
SET_SIM_CODE(
    "$(addToInSyn, $(inc));\n"
```
where “inc” is the increment of the synaptic input to a post-synaptic neuron for each pre-synaptic spike. The simulation code also typically contains updates to the internal synapse variables that may have contributed to . For an example, see WeightUpdateModels::StaticPulse for a simple synapse update model and WeightUpdateModels::PiecewiseSTDP for a more complicated model that uses STDP. To apply input to the post-synaptic neuron with a dendritic (i.e. between the synapse and the postsynaptic neuron) delay you can instead use the $(addToInSynDelay, weight, delay) function. For example
```
SET_SIM_CODE(
    "$(addToInSynDelay, $(inc), $(delay));");
```
where, once again, inc is the magnitude of the input step to apply and delay is the length of the dendritic delay in timesteps. By implementing delay as a weight update model variable, heterogeneous synaptic delays can be implemented. For an example, see WeightUpdateModels::StaticPulseDendriticDelay for a simple synapse update model with heterogeneous dendritic delays. When using dendritic delays, the maximum dendritic delay for a synapse populations must be specified using the SynapseGroup::setMaxDendriticDelayTimesteps() function.
SET_EVENT_THRESHOLD_CONDITION_CODE(EVENT_THRESHOLD_CONDITION_CODE) defines a condition for a synaptic event. This typically involves the pre-synaptic variables, e.g. the membrane potential:
```
SET_EVENT_THRESHOLD_CONDITION_CODE("$(V_pre) > -0.02");
```
Whenever this expression evaluates to true, the event code set using the SET_EVENT_CODE() macro is executed. For an example, see WeightUpdateModels::StaticGraded.
SET_EVENT_CODE(EVENT_CODE) defines the code that is used when the event threshold condition is met (as set using the SET_EVENT_THRESHOLD_CONDITION_CODE() macro).
SET_LEARN_POST_CODE(LEARN_POST_CODE) defines the code which is used in the learnSynapsesPost kernel/function, which performs updates to synapses that are triggered by post-synaptic spikes. This is typically used in STDP-like models e.g. WeightUpdateModels::PiecewiseSTDP.
SET_SYNAPSE_DYNAMICS_CODE(SYNAPSE_DYNAMICS_CODE) defines code that is run for each synapse, each timestep i.e. unlike the others it is not event driven. This can be used where synapses have internal variables and dynamics that are described in continuous time, e.g. by ODEs. However using this mechanism is typically computationally very costly because of the large number of synapses in a typical network. By using the $(addtoinsyn), $(updatelinsyn) and $(addToDenDelay) mechanisms discussed in the context of SET_SIM_CODE(), the synapse dynamics can also be used to implement continuous synapses for rate-based models.
SET_PRE_SPIKE_CODE() and SET_POST_SPIKE_CODE() define code that is called whenever there is a pre or postsynaptic spike. Typically these code strings are used to update any pre or postsynaptic state variables.
SET_NEEDS_PRE_SPIKE_TIME(PRE_SPIKE_TIME_REQUIRED) and SET_NEEDS_POST_SPIKE_TIME(POST_SPIKE_TIME_REQUIRED) define whether the weight update needs to know the times of the spikes emitted from the pre and postsynaptic populations. For example an STDP rule would be likely to require:
```
SET_NEEDS_PRE_SPIKE_TIME(true);
SET_NEEDS_POST_SPIKE_TIME(true);
```

All code snippets, aside from those defined with SET_PRE_SPIKE_CODE() and SET_POST_SPIKE_CODE(), can be used to manipulate any synapse variable and so learning rules can combine both time-drive and event-driven processes.

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