Current source models

There is a number of predefined models which can be used with the ModelSpec::addCurrentSource function:

• CurrentSourceModels::DC
• CurrentSourceModels::GaussianNoise
• CurrentSourceModels::PoissonExp

In Python, these models can be selected by their unqualified name e.g. "DC".

Defining your own current source model

In order to define a new current source type for use in a GeNN application, it is necessary to define a new class derived from CurrentSourceModels::Base. For convenience the methods this class should implement can be implemented using macros keyword arguments :

• DECLARE_MODEL(TYPE, NUM_PARAMS, NUM_VARS), SET_DERIVED_PARAMS(), SET_PARAM_NAMES(), SET_VARS() derived_params, param_names, var_name_types perform the same roles as they do in the neuron models discussed in Defining your own neuron type.
• SET_INJECTION_CODE(INJECTION_CODE)injection_code=INJECTION_CODE : where INJECTION_CODE contains the code for injecting current into the neuron every simulation timestep. The $(injectCurrent, ) function is used to inject current.

For example, using these macroskeyword arguments, we can define a uniformly distributed noisy current source:

class UniformNoise : public CurrentSourceModels::Base
{
public:
    DECLARE_MODEL(UniformNoise, 1, 0);

    SET_INJECTION_CODE("$(injectCurrent, $(gennrand_uniform) * $(magnitude));");

    SET_PARAM_NAMES({"magnitude"});
};

uniform_noise_model = genn_model.create_custom_current_source_class(
    "uniform_noise",

    param_names=["magnitude"],
    injection_code="$(injectCurrent, $(gennrand_uniform) * $(magnitude));")

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