Dynamic shifts in brain criticality support cognitive processing

Abstract Systems operating near their critical point, or close to a transition between order and disorder, have computational advantages. In the case of neural networks, proximity to criticality is proposed to support optimal brain function. However, different cognitive processes rely on disparate computational demands. Using large-scale electrophysiological recordings in behaving rodents, we examined how critical dynamics in the hippocampus are regulated during learning and sleep-dependent memory consolidation. We found that operating near criticality enables learning by facilitating hippocampal coordination with input regions and maximizing flexibility of neural representations. In contrast, the hippocampal network shifts toward a more ordered, subcritical regime during sleep memory replay, and recovers its proximity to criticality through cholecystokinin interneurons-mediated inhibition. Overall, our findings provide a biophysical substrate for understanding how critical dynamics in neuronal networks can support a variety of brain functions. Importantly, our results suggest that optimal learning systems, whether biological or artificial, may require a dynamic regulation between flexible and rigid states, and can offer biophysical constraints to guide the design of Large Language Models (LLM) tuned to criticality.