Understanding Cognition Means Understanding Systems

The awarding of the 2024 Nobel Prize in Physics to Geoffrey Hinton and John Hopfield marked a moment of scientific transition. Two researchers, known not primarily for classical experiments at particle accelerators or observatories, but for their work on neural networks—both artificial and biological—were honored for an achievement that reaches deeply into our understanding of the mind. Their work demonstrates that cognition is not an exclusive product of the human brain, but an emergent phenomenon of dynamic, interconnected systems. Whether biological neurons, artificial networks, or cellular collectives—wherever information is processed, stabilized, transformed, and applied in a goal-directed manner, mind-like processes emerge. For a long time, the study of cognition was primarily a matter of measurement. Using fMRI, EEG, and other imaging techniques, researchers mapped where in the brain language is processed, attention is controlled, or memories are stored. These methods undoubtedly provide important insights—but they explain only what happens, not how or why it happens. Cognition cannot be derived from activity patterns alone; it must be understood as the result of physical, biological, and information-theoretical principles.This is where the relevance of theoretical and computational neuroscience emerges. The models developed by Hopfield and Hinton translate neuronal activity into mathematical dynamics. They demonstrate how representations arise, how systems learn and adapt, and how complex behavior emerges from simple structures. This perspective connects the brain with the machine—and both with general principles of self-organization and information processing.