Fig. 1. Diagram of network architecture and training configurations.

A Model architecture. A layer of input neurons emits spike trains into a recurrently connected layer of spiking neurons which is followed by a readout layer. B Configurations. Training can be either standard (only the synaptic weights are learned) or heterogeneous (the synaptic weights and membrane and synaptic time constants are learned). The initialisation can be homogeneous (all synaptic and membrane time constants are initialised to the same value) or heterogeneous (synaptic and membrane time constants are randomly initialised for each neuron by sampling them from a given probability distribution).