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

TL;DR

This paper reviews 25 years of research on neural network criticality, highlighting controversies, unifying contradictory findings, and proposing a new adaptive tuning approach for network properties.

Contribution

It unifies conflicting experimental results on neural criticality and introduces a novel adaptive tuning mechanism that addresses previous disadvantages.

Findings

Unifies contradictory experimental results on neural criticality.

Proposes a new adaptive tuning mechanism for neural networks.

Addresses disadvantages of the critical state hypothesis.

Abstract

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.