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SparseConnections are used to connect neuron groups with static synapses that are generally initialized randomly with sparse entries, but the connection can just as well be loaded from a wmat file. A standard call to the class might look like the following
SparseConnection * con_ie = new SparseConnection(neurons_i,neurons_e, w_ie,sparseness,GABA);
where the neuron_i has to be a child of SpikingGroup and neurons_e has to be derived from NeuronGroup. The third parameter (w_ie in this case) represents the value of the synaptic strength in units of the leak conductance of the postsynaptic neuron. When initialized with a given weight and sparseness the constructor will connect the two groups in an all-to-all fashion with an overall connection probability which is given by sparseness. The last parameter (GABA in the example) specifies the target state variable to act on. Allowed values are of TransmitterType such as GLUT (mostly a synonym for AMPA and NMDA depending on the neuron model), GABA for inhibitory conductances. It is also possible to only target NMDA channels or the membrane voltage directly (MEM), which allows to implement current based models. In the latter case the synaptic weight has to be interpreted in units of voltage.