  How do we understand natural intelligence? Inspired by classical embodied
cognition and the emerging multimodal interaction, we study the organizational
principles of neural systems at three levels (device/implementation,
circuit/algorithm, and system/computational) in this survey paper. Our main
contributions consist of 1) Device/Implementation level: reproducibility of
macroscopic states in polychronization neural group (PNG) serves as the
physical basis of associative memory; 2) Circuit/Algorithm level: canonical
microcircuits implement a universal predictive coding algorithm underlying all
high-level cognitive functions; 3) System/Computational level: modeling
sensorimotor interaction for embodied cognition plays a fundamental role in
understanding natural intelligence. At each level, we use mathematical models
as our abstractions and study their organizational principles (e.g., entropy
reduction, predictive coding, and coordinate transformation). The unifying
theme is that natural intelligence evolves by recycling a simple navigation
trick in different coordinates (reference frames) hundreds of thousands of
times. By representing space as a latent sequence, the neocortex solves the
embodied cognition problem by building parallel cognitive maps at a large
scale. We hope this survey article can inspire new research at the intersection
of neuroscience and learning systems, helping bridge the gap between natural
and artificial intelligence.
