  The dynamic range of stimulus processing in living organisms is much larger
than a single neural network can explain. For a generic, tunable spiking
network we derive that while the dynamic range is maximal at criticality, the
interval of discriminable intensities is very similar for any network tuning
due to coalescence. Compensating coalescence enables adaptation of
discriminable intervals. Thus, we can tailor an ensemble of networks optimized
to the distribution of stimulus intensities, e.g., extending the dynamic range
arbitrarily. We discuss potential applications in machine learning.
