Spiking neurons consume energy for each spike they emit. Reducing the firing rate of each neuron — without sacrificing relevant information content — is therefore a critical constraint for energy efficient networks of spiking neurons in biology and neuromorphic hardware alike. The inherent complexity of biological neurons provides a possible mechanism to realize a good trade-off between these two conflicting objectives: multi-compartment neuron models can become selective to highly specific input patterns, and can thus produce informative yet sparse spiking codes. I’ll present a model of this mechanism and discuss its potential utility for spiking neural networks and neuromorphic hardware. This talk won the best presentation award.