We present the Modular Neural Network (MNN), a brain-inspired computational architecture that fundamentally transcends the limitations of current large-scale Transformer and Mixture-of-Experts (MoE) models. MNN employs five core mechanisms working in concert: (1) dynamic semantic routing with persistent memory, enabling adaptive expert allocation based on input complexity; (2) independent experts withinternal recursion and dual-role local dynamic penalty for self-stabilization; (3) bijectionflow adapters with compression projection and mutual information maximization forefficient cross-modal communication; (4) directional precision-weighted permeation forasymmetric, selective inter-expert information flow; and (5) global energy functional-driven consensus with posterior verification and self-correction. We provide complete mathematical formalization of all mechanisms and propose a training framework based on implicit differentiation that resolves the computational challenges of dynamic iteration depth. An engineering deployment scheme, encompassingtime-box scheduling, stateless horizontal scaling, elastic degradation, and hardware-level isolation, provides a practical blueprint for implementation on existing GPU infrastructure. The MNN architecture simulates the hierarchical compression, consensus optimization, and metacognitive arbitration mechanisms of biological intelligence, providing a theoretically self-consistent and engineeringly executable blueprint for building scalable, interpretable, and continuously evolving generalartificial intelligence systems.