  The critical state is assumed to be optimal for any computation in recurrent
neural networks, because criticality maximizes a number of abstract
computational properties. We challenge this assumption by evaluating the
performance of a spiking recurrent neural network on a set of tasks of varying
complexity at - and away from critical network dynamics. To that end, we
developed a spiking network with synaptic plasticity on a neuromorphic chip. We
show that the distance to criticality can be easily adapted by changing the
input strength, and then demonstrate a clear relation between criticality,
task-performance and information-theoretic fingerprint. Whereas the
information-theoretic measures all show that network capacity is maximal at
criticality, this is not the case for performance on specific tasks: Only the
complex, memory-intensive tasks profit from criticality, whereas the simple
tasks suffer from it. Thereby, we challenge the general assumption that
criticality would be beneficial for any task, and provide instead an
understanding of how the collective network state should be tuned to task
requirement to achieve optimal performance.
