Neural Infrastructure Computing: A Conceptual Framework for Persistent Knowledge-Centric Neural Networks

Artificial neural networks have transformed numerous scientific and industrial domains through their ability to learn complex representations from data. Contemporary applications primarily emphasize prediction, classification, generation and optimization, positioning neural networks as computational models that infer relationships from observed information. Although these capabilities have achieved remarkable success, existing paradigms largely treat neural networks as task-oriented models whose operational lifetime begins with training and concludes with inference. Such a perspective overlooks the possibility that neural architectures may evolve into persistent computational infrastructures capable of continuously accumulating, organizing and operationalizing knowledge across heterogeneous environments. This conceptual preprint introduces Neural Infrastructure Computing (NIC), defined as a computational paradigm in which neural networks function as persistent organizational infrastructures for knowledge acquisition, integration, reasoning and adaptive learning rather than isolated predictive models. Unlike conventional machine learning systems that are optimized for individual tasks, NIC conceptualizes neural architectures as continuously evolving computational ecosystems supporting long-term knowledge persistence across scientific, industrial and enterprise domains. The manuscript proposes a theoretical framework describing how persistent neural infrastructures integrate distributed information sources, adaptive learning mechanisms, computational memory and collaborative intelligence into unified knowledge ecosystems. The framework further explores implications for scientific discovery, engineering design, enterprise intelligence and autonomous computational systems. Rather than viewing neural networks solely as analytical algorithms, the proposed perspective positions them as foundational infrastructures supporting future computational knowledge systems