The brain exhibits a wide range of spatiotemporal activity patterns essential for adaptive behavior. Understanding how the brain's 100 billion neurons and 100 trillion synapses generate flexible, complex configurations remains a major challenge in neuroscience. Recent research suggests that the brain may naturally operate near a critical point of a second-order phase transition, similar to emergent complex phenomena in dynamical systems. This criticality could explain the brain's ability to produce diverse, self-organized behaviors. The brain, with its vast number of nonlinear elements, exhibits collective dynamics akin to those studied in statistical physics. However, most neuroscience research focuses on explicit connectionist models rather than collective processes. This paper argues that understanding the brain as a collective system is crucial for resolving fundamental questions about its function. Emergent complex neural dynamics are characterized by scale-free patterns, such as neuronal avalanches, which follow power-law distributions and exhibit self-similar properties. These patterns are observed in both small-scale cortical activity and large-scale brain networks. Spontaneous brain activity, even in the absence of external input, shows complex, non-random dynamics, with scale-invariant features across multiple time scales. Neuronal avalanches, a form of self-organized criticality, demonstrate scale-free behavior and long-range correlations. These patterns are consistent with critical dynamics, as evidenced by their statistical properties, including inverse power law exponents and fractal spatial spread. Studies using functional magnetic resonance imaging (fMRI) have shown that resting-state brain networks exhibit properties similar to those of critical systems, such as the Ising model. The brain's critical state allows it to maintain a balance between stability and adaptability, enabling it to respond to a wide range of stimuli. This criticality is essential for learning, memory, and flexible behavior. The paper highlights the importance of criticality in brain function, suggesting that understanding it could lead to new insights into brain organization and dysfunction in neurological disorders. Overall, the brain's complex dynamics are best understood through the lens of criticality, which provides a framework for explaining its ability to generate diverse, self-organized behaviors. This perspective challenges traditional models and emphasizes the need for interdisciplinary approaches to fully understand the brain's function.The brain exhibits a wide range of spatiotemporal activity patterns essential for adaptive behavior. Understanding how the brain's 100 billion neurons and 100 trillion synapses generate flexible, complex configurations remains a major challenge in neuroscience. Recent research suggests that the brain may naturally operate near a critical point of a second-order phase transition, similar to emergent complex phenomena in dynamical systems. This criticality could explain the brain's ability to produce diverse, self-organized behaviors. The brain, with its vast number of nonlinear elements, exhibits collective dynamics akin to those studied in statistical physics. However, most neuroscience research focuses on explicit connectionist models rather than collective processes. This paper argues that understanding the brain as a collective system is crucial for resolving fundamental questions about its function. Emergent complex neural dynamics are characterized by scale-free patterns, such as neuronal avalanches, which follow power-law distributions and exhibit self-similar properties. These patterns are observed in both small-scale cortical activity and large-scale brain networks. Spontaneous brain activity, even in the absence of external input, shows complex, non-random dynamics, with scale-invariant features across multiple time scales. Neuronal avalanches, a form of self-organized criticality, demonstrate scale-free behavior and long-range correlations. These patterns are consistent with critical dynamics, as evidenced by their statistical properties, including inverse power law exponents and fractal spatial spread. Studies using functional magnetic resonance imaging (fMRI) have shown that resting-state brain networks exhibit properties similar to those of critical systems, such as the Ising model. The brain's critical state allows it to maintain a balance between stability and adaptability, enabling it to respond to a wide range of stimuli. This criticality is essential for learning, memory, and flexible behavior. The paper highlights the importance of criticality in brain function, suggesting that understanding it could lead to new insights into brain organization and dysfunction in neurological disorders. Overall, the brain's complex dynamics are best understood through the lens of criticality, which provides a framework for explaining its ability to generate diverse, self-organized behaviors. This perspective challenges traditional models and emphasizes the need for interdisciplinary approaches to fully understand the brain's function.