The article by Stephen Grossberg provides an historical overview of the interdisciplinary studies in the 19th century, highlighting the contributions of scientists like Helmholtz, Maxwell, and Mach. It discusses the schism between physics and psychology that emerged in the late 19th century, emphasizing the nonlinear, nonlocal, and nonstationary nature of behavioral and brain data. The author outlines three sources of contemporary neural network research: binary, linear, and continuous-nonlinear models. The focus is on continuous-nonlinear models, which are shown to encompass many content-addressable memory models, including the Cohen-Grossberg model and global Liapunov function. The article describes methods for proving global limit or oscillation theorems for nonlinear competitive systems and discusses key properties of shunting competitive feedback networks. It also compares adaptive resonance theory (ART) models with offline models and explores the role of top-down expectations and attentional processing in learning and information processing. The author concludes by reflecting on the enduring synthesis of interdisciplinary research and the need for a unified framework to explain the complex dynamics of mind and brain.The article by Stephen Grossberg provides an historical overview of the interdisciplinary studies in the 19th century, highlighting the contributions of scientists like Helmholtz, Maxwell, and Mach. It discusses the schism between physics and psychology that emerged in the late 19th century, emphasizing the nonlinear, nonlocal, and nonstationary nature of behavioral and brain data. The author outlines three sources of contemporary neural network research: binary, linear, and continuous-nonlinear models. The focus is on continuous-nonlinear models, which are shown to encompass many content-addressable memory models, including the Cohen-Grossberg model and global Liapunov function. The article describes methods for proving global limit or oscillation theorems for nonlinear competitive systems and discusses key properties of shunting competitive feedback networks. It also compares adaptive resonance theory (ART) models with offline models and explores the role of top-down expectations and attentional processing in learning and information processing. The author concludes by reflecting on the enduring synthesis of interdisciplinary research and the need for a unified framework to explain the complex dynamics of mind and brain.