Biological intelligence arises from neural systems capable of learning and controlling dynamic behavior with remarkable efficiency, robustness, and adaptability. Organisms exist in a continuous feedback loop with their environment, using diverse and richly structured spatiotemporal neural dynamics to regulate behavior and flexibly adapt to changing conditions. Learning in biological systems unfolds over their lifespan, enabling the construction and continual refinement of internal models of the world and body - supporting perception, prediction, decision-making, and goal-directed action.
Yet we still lack a unifying understanding of the principles that coordinate the myriad mechanisms in the nervous system that underlie biological intelligence. Despite advances in experimental neuroscience, limitations in resolution, interpretability, and system-wide integration hinder comprehensive understanding. To close this gap, we need interdisciplinary approaches that combine insights from biology with computational, theoretical, and data-driven methodologies.

Therefore, this Research Topic welcome’s contributions that advance our understanding of biological intelligence through approaches including, but not limited to:

- Mechanistic models that explain adaptive behavior.
- Theorizing about objective functions or cost principles being optimized.
- Machine learning to inform theoretical models of neural or behavioral data.

This Research Topic invites Research and Review articles that uncover computational and dynamical principles of biological intelligence and their implications for neural codes, control, and system-level organization. We also encourage Perspective or Opinion pieces proposing conceptual frameworks or community strategies to move this field forward, especially those that foster cross-disciplinary dialogue and collaboration.

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Principles of Biological Intelligence