TNA in Cognitive Networks and Artificial Intelligence: Throughput, Incoherence, and Structural Stability

Cognitive networks and artificial intelligence (AI) systems are traditionally modeled through algorithmic optimization, weight adaptation, and error minimization. This paper proposes a TNA-based framework, viewing cognitive networks as open systems where computational coherence requires continuous throughput (Ψ) to discharge incoming incoherence (G). When G exceeds Ψ, accumulated incoherence (ℵ) manifests as learning instability, catastrophic forgetting, or performance collapse. Phenomena such as neural network overfitting, feedback loops, and system bottlenecks can be reframed as throughput saturation rather than algorithmic inefficiency. The model is falsifiable, predictive, and applicable to both biological cognitive models and artificial networks.