This article presents an operational approach to understanding pattern generation in complex biological systems, combining theory and experiment. It uses mathematical concepts of self-organization in nonequilibrium systems, such as order parameter dynamics, stability, fluctuations, and time scales, to explain how empirically observed temporal patterns can be modeled by simple low-dimensional stochastic, nonlinear dynamical laws. The theoretical framework provides a language and strategy for understanding dynamic patterns across multiple scales, including behavioral patterns, neural networks, and individual neurons, and the relationships among them. The article discusses the dynamics of pattern formation, emphasizing the role of self-organization in nonequilibrium systems. It introduces the concept of dissipative dynamics, where trajectories of a system converge to an attractor. The dynamics of pattern formation are described by a general equation that includes nonlinear functions of microscopic state vectors, parameters, and noise. The article highlights the importance of critical points in parameter space, where solutions may change qualitatively or discontinuously, leading to phase transitions and the spontaneous formation of spatial or temporal patterns. The article proposes that biological coordination and function can be understood as dynamic patterns, characterized by collective variables or order parameters. It presents several propositions, including the idea that behavioral patterns can be characterized by collective variables, that experimentally defined patterns correspond to stable collective states, that loss of stability leads to changes in behavioral patterns, and that fluctuations in order parameters reconcile stability with the ability to change patterns. The article also discusses the relationship between different levels of description, from individual neurons to neural networks, and how dynamic pattern theory can be applied to understand neural systems. The article concludes by emphasizing the importance of dynamic pattern theory in understanding biological systems, particularly in the context of neural networks and central pattern generators. It highlights the potential of dynamic pattern theory to provide a conceptual framework for understanding the stability and change of behavioral patterns, as well as the influence of perception, memory, and learning on these patterns. The article also notes the importance of identifying order parameters and their dynamics in extending the approach to consider the influence of these factors on behavioral patterns.This article presents an operational approach to understanding pattern generation in complex biological systems, combining theory and experiment. It uses mathematical concepts of self-organization in nonequilibrium systems, such as order parameter dynamics, stability, fluctuations, and time scales, to explain how empirically observed temporal patterns can be modeled by simple low-dimensional stochastic, nonlinear dynamical laws. The theoretical framework provides a language and strategy for understanding dynamic patterns across multiple scales, including behavioral patterns, neural networks, and individual neurons, and the relationships among them. The article discusses the dynamics of pattern formation, emphasizing the role of self-organization in nonequilibrium systems. It introduces the concept of dissipative dynamics, where trajectories of a system converge to an attractor. The dynamics of pattern formation are described by a general equation that includes nonlinear functions of microscopic state vectors, parameters, and noise. The article highlights the importance of critical points in parameter space, where solutions may change qualitatively or discontinuously, leading to phase transitions and the spontaneous formation of spatial or temporal patterns. The article proposes that biological coordination and function can be understood as dynamic patterns, characterized by collective variables or order parameters. It presents several propositions, including the idea that behavioral patterns can be characterized by collective variables, that experimentally defined patterns correspond to stable collective states, that loss of stability leads to changes in behavioral patterns, and that fluctuations in order parameters reconcile stability with the ability to change patterns. The article also discusses the relationship between different levels of description, from individual neurons to neural networks, and how dynamic pattern theory can be applied to understand neural systems. The article concludes by emphasizing the importance of dynamic pattern theory in understanding biological systems, particularly in the context of neural networks and central pattern generators. It highlights the potential of dynamic pattern theory to provide a conceptual framework for understanding the stability and change of behavioral patterns, as well as the influence of perception, memory, and learning on these patterns. The article also notes the importance of identifying order parameters and their dynamics in extending the approach to consider the influence of these factors on behavioral patterns.