The article by G. Schöner and J. A. S. Kelso presents an operational approach to understanding pattern generation in complex biological systems, combining theory and experiment. The authors use mathematical concepts from self-organization in nonequilibrium systems, such as order parameter dynamics, stability, fluctuations, and time scales, to map empirically observed temporal patterns onto simple, low-dimensional dynamical laws. This framework provides a language and strategy for understanding dynamic patterns at various scales, including behavioral patterns, neural networks, and individual neurons, and their interconnections. The authors highlight the challenge of coordinating complex biological systems to produce functionally specific ordered behavior, emphasizing the need to identify the principles governing pattern generation. They argue that cooperative phenomena in nature are often independent of specific molecular machinery, suggesting that principles of coordination may lie at the level of patterns themselves. The article discusses the dynamics of pattern formation, introducing key theoretical concepts such as dissipative structures and nonequilibrium phase transitions. It explains how these concepts can be applied to biological systems, where the path from microscopic to macroscopic is more challenging than in physical systems. The authors propose that behavioral patterns can be characterized by collective variables or order parameters, and that these patterns can be derived from the cooperative coupling of individual components. The article provides examples of rhythmic movement patterns, such as the in-phase and anti-phase patterns of two hands moving at a common frequency, to illustrate the application of these concepts. It also discusses the stability of these patterns, the role of fluctuations, and the conditions under which patterns change. The authors emphasize the importance of time scales in understanding pattern stability and change, and how these scales can be measured and manipulated. Finally, the article explores the relationship between different levels of description in neurobiological systems, from network behavior to individual neuron dynamics. It suggests that the dynamic pattern approach can provide a conceptual framework for understanding central pattern generators and motor programs, and highlights the potential for linking macroscopic behavioral levels to microscopic physiological levels.The article by G. Schöner and J. A. S. Kelso presents an operational approach to understanding pattern generation in complex biological systems, combining theory and experiment. The authors use mathematical concepts from self-organization in nonequilibrium systems, such as order parameter dynamics, stability, fluctuations, and time scales, to map empirically observed temporal patterns onto simple, low-dimensional dynamical laws. This framework provides a language and strategy for understanding dynamic patterns at various scales, including behavioral patterns, neural networks, and individual neurons, and their interconnections. The authors highlight the challenge of coordinating complex biological systems to produce functionally specific ordered behavior, emphasizing the need to identify the principles governing pattern generation. They argue that cooperative phenomena in nature are often independent of specific molecular machinery, suggesting that principles of coordination may lie at the level of patterns themselves. The article discusses the dynamics of pattern formation, introducing key theoretical concepts such as dissipative structures and nonequilibrium phase transitions. It explains how these concepts can be applied to biological systems, where the path from microscopic to macroscopic is more challenging than in physical systems. The authors propose that behavioral patterns can be characterized by collective variables or order parameters, and that these patterns can be derived from the cooperative coupling of individual components. The article provides examples of rhythmic movement patterns, such as the in-phase and anti-phase patterns of two hands moving at a common frequency, to illustrate the application of these concepts. It also discusses the stability of these patterns, the role of fluctuations, and the conditions under which patterns change. The authors emphasize the importance of time scales in understanding pattern stability and change, and how these scales can be measured and manipulated. Finally, the article explores the relationship between different levels of description in neurobiological systems, from network behavior to individual neuron dynamics. It suggests that the dynamic pattern approach can provide a conceptual framework for understanding central pattern generators and motor programs, and highlights the potential for linking macroscopic behavioral levels to microscopic physiological levels.