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

arXiv: https://arxiv.org/abs/1903.05129 · HTML: https://mlsanity.com/paper/1903.05129/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Fading memory as inductive bias in residual recurrent networks](https://mlsanity.com/paper/2307.14823/) (2023)
- [Self-Organising Memristive Networks as Physical Learning Systems](https://mlsanity.com/paper/2509.00747/) (2025)
- [Biases in Inverse Ising Estimates of Near-Critical Behaviour](https://mlsanity.com/paper/2301.05556/) (2023)
- [Sleep EEG Signal Criticality as a Non-Invasive Predictor of Cognitive Decline in Dementia](https://mlsanity.com/paper/2606.10889/) (2026)

## Abstract (arXiv)

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.