Imagine a crowded dance floor where everyone is moving in perfect sync, creating a harmonious and captivating spectacle. This is akin to the critical phase transition point in our brain, where living neural networks operate at their optimal performance. The criticality hypothesis suggests that this delicate balance between chaos and order is crucial for efficient information processing and overall brain health. However, some skeptics have raised objections, questioning whether this hypothesis holds true. In this paper, the objections put forth by Touboul and Destexhe are examined and countered with compelling responses. One objection claims that the Brunel model for cortical networks does not exhibit a peak in mutual information near its phase transition point, seemingly contradicting the criticality hypothesis. However, it is shown that such a peak does exist under certain conditions, providing evidence in support of the hypothesis. Another objection posits that simple models like a coin flip can also demonstrate characteristics of criticality. While this may be true, it is emphasized that these models lack the essential properties observed in real neural networks, such as collective interactions, information processing capabilities, and long-range temporal correlations. Despite these objections, they have ultimately played a valuable role in refining research questions and promoting healthy scientific discourse. To delve deeper into the intriguing world of critical brain functioning and explore the responses to these objections, check out the full article!

The hypothesis that living neural networks operate near a critical phase transition point has received substantial discussion. This “criticality hypothesis” is potentially important because experiments and theory show that optimal information processing and health are associated with operating near the critical point. Despite the promise of this idea, there have been several objections to it. While earlier objections have been addressed already, the more recent critiques of Touboul and Destexhe have not yet been fully met. The purpose of this paper is to describe their objections and offer responses. Their first objection is that the well-known Brunel model for cortical networks does not display a peak in mutual information near its phase transition, in apparent contradiction to the criticality hypothesis. In response I show that it does have such a peak near the phase transition point, provided it is not strongly driven by random inputs. Their second objection is that even simple models like a coin flip can satisfy multiple criteria of criticality. This suggests that the emergent criticality claimed to exist in cortical networks is just the consequence of a random walk put through a threshold. In response I show that while such processes can produce many signatures criticality, these signatures (1) do not emerge from collective interactions, (2) do not support information processing, and (3) do not have long-range temporal correlations. Because experiments show these three features are consistently present in living neural networks, such random walk models are inadequate. Nevertheless, I conclude that these objections have been valuable for refining research questions and should always be welcomed as a part of the scientific process.

Read Full Article (External Site)