Scientists have uncovered a "scale-invariant" phenomenon in neurons, offering new insights into how the brain encodes information. The study combines neuroscience, machine learning, theoretical physics, and mathematics, providing a solid foundation for their findings. In the study, researchers used light-field imaging technology to record the neural activity of an entire zebrafish brain in real time. This method allows them to capture data from all neurons simultaneously. During this process, they observed a peculiar phenomenon known as "scale invariance," where the scale at which neural activity is measured does not affect the quantity of information recorded. This means that to obtain sufficient neural coding information, it is not necessary to record the activity of every single neuron. This discovery bears similarities to the "critical state" concept in statistical physics, which refers to a system poised between order and disorder. Although the observed scale invariance has not yet been definitively linked to a specific critical state in the brain, it suggests that the brain might operate in some unknown critical state. This adds a significant theoretical framework to the understanding of large-scale concurrent neural activities. Wencong Chou, one of the researchers, explained, "This study is truly groundbreaking. Not only did we develop novel mathematical methods to interpret this phenomenon, but we also leveraged techniques from statistical physics, some of which have rarely been applied to neuroscience." They further developed new theories to determine under what conditions this phenomenon can be observed, indicating that their approach involves interpreting the overall structure from a top-down perspective, rather than the traditional bottom-up approach. Unlike conventional bottom-up studies that focus on individual neurons or circuits, this research takes a top-down angle to understand the brain's overall structure. This shift in perspective is crucial because it requires scientists to grasp the brain from different levels, much like Nobel Prize-winning physicist Philip Warren Anderson's concept of "more is different." Currently, the team is applying their theory to practical research, developing imaging technologies to enhance speed and precision. For example, they are improving optical imaging techniques to enable real-time imaging and analysis. By creating closed-loop systems, they aim to better understand the organizational patterns of whole-brain neural activity and conduct relevant experimental studies. Chou commented, "Our technique, which we call a 'photonic brain interface,' differs from traditional electrical-based interfaces. It uses light to read and write information, which represents a significant development direction for us. We believe this is a highly important and promising innovation." This research opens up new avenues for exploring the brain's complex information processing mechanisms, combining advanced mathematical models and innovative imaging technologies to push the boundaries of neuroscience.