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Twenty-five years ago, Dunkelmann and Radons (1994) proposed that neuralnetworks should self-organize to a critical state. In models, criticalityoffers a number of computational advantages. Thus this hypothesis, and inparticular the experimental work by Beggs and Plenz (2003), has triggered anavalanche of research, with thousands of studies referring to it. Nonetheless,experimental results are still contradictory. How is it possible, that ahypothesis has attracted active research for decades, but nonetheless remainscontroversial? We discuss the experimental and conceptual controversy, and thenpresent a parsimonious solution that (i) unifies the contradictory experimentalresults, (ii) avoids disadvantages of a critical state, and (iii) enablesrapid, adaptive tuning of network properties to task requirements.

25 years of criticality in neuroscience -- established results, open controversies, novel concepts