  There are indications that for optimizing neural computation, neural networks
- including the brain - operate at criticality. Previous approaches have,
however, used diverse fingerprints of criticality, leaving open the question
whether they refer to a unique critical point or whether there could be
several. Using a recurrent spiking neural network as the model, we demonstrate
that avalanche criticality does not necessarily lie at the dynamical
edge-of-chaos and that therefore, the different fingerprints indicate distinct
phenomena with an as yet unclarified relationship.
