Abstract: Based on a deep understanding of the visual encoding neurophysiological mechanisms between the retina, lateral body, and visual cortex of the brain, we have constructed a novel neural network-based brain-inspired intelligent unit architecture, laying a solid foundation for the implementation of large-scale integrated neural network-based brain-inspired models. This information theory foundation for our understanding of the expression and computation of brain sensory encoding is formed by the architecture of brain-inspired intelligent units. This article delves into the training methods, strategies, and specific algorithm examples of brain-inspired models, and proposes a comprehensive strategy. This strategy combines the redundancy reduction principle of sensory data flow expression and computation, self-organizing feature mapping, and backtracking oscillation synchronization mechanism, aiming to improve the biological rationality and interpretability of brain-inspired models, as well as efficiently and quickly mimic complex brain functions.