Living Neural Compute Architecture (LNCA): Autologous Pretrained Neural Grafts, Biological Foundation Models, Experience Compression, and Sleep-State Continual Learning

Living Neural Compute Architecture (LNCA) proposes a falsifiable research framework for creating, training, transplanting, and subsequently adapting living neural modules derived from autologous human cells. The central hypothesis is that an engineered neural network may acquire task-relevant functional representations ex vivo through AI-guided training, preserve a useful fraction of that learned state across transplantation, and become functionally accessible to the host nervous system through bidirectional biological integration and co-adaptation. The framework introduces the concepts of Autologous Pretrained Neural Grafts (APNGs), Biological Foundation Models, Experience Compression, Neural Dream Training, and Counterfactual Dreaming. Rather than assuming literal transfer of artificial neural-network weights into biological tissue, LNCA treats artificial intelligence as a teacher that may shape biological network dynamics through closed-loop stimulation, feedback, curriculum learning, and functional distillation. A second research direction considers post-integration continual learning: synthetic experience generated by an AI world model could potentially be delivered to an integrated neural graft during appropriate offline or sleep-associated states, allowing task-specific rehearsal, counterfactual simulation, and consolidation without requiring corresponding real-world exposure. The paper distinguishes established scientific components from demonstrated partial analogues, testable hypotheses, and speculative long-term endpoints. It proposes quantitative evaluation metrics, falsification criteria, staged experimental classes, and a development roadmap from in-vitro neural networks to host-graft co-adaptation. It does not claim that current organoids possess human-like cognition, that complex skills can presently be implanted into humans, or that accelerated biological learning equivalent to years of human experience has been demonstrated. Artificial Brain II project-lineage paper; companion work to Continuous Neural Continuity Architecture (CNCA).