  Twenty-five years ago, Dunkelmann and Radons (1994) proposed that neural
networks should self-organize to a critical state. In models, criticality
offers a number of computational advantages. Thus this hypothesis, and in
particular the experimental work by Beggs and Plenz (2003), has triggered an
avalanche of research, with thousands of studies referring to it. Nonetheless,
experimental results are still contradictory. How is it possible, that a
hypothesis has attracted active research for decades, but nonetheless remains
controversial? We discuss the experimental and conceptual controversy, and then
present a parsimonious solution that (i) unifies the contradictory experimental
results, (ii) avoids disadvantages of a critical state, and (iii) enables
rapid, adaptive tuning of network properties to task requirements.
