Engram Formation System: Computational Implementation of bioinspired memory for Robotics and AI Research

We present a neurobiomorphic system implementing biological engram formation through computational pattern stabilization detection. The system combines semantic relationship tensors, living cellular automata with predictive processing, and temporal binding mechanisms to test the hypothesis that associative memory serves as the fundamental predictive element underlying biological neural systems. Operating on real-time multimodal sensory input (vision, optical flow, extracted features), the system automatically forms engrams when prediction-reality convergence stabilizes beyond defined criteria (variance $< 0.01$ over 25+ frames). Engrams are linked via temporal contiguity, spatial similarity, and probable causality, mimicking biological memory consolidation. This work provides: (1) a computational framework for testing biological consciousness principles, (2) emergence detection methodology critical for AI safety research, and (3) preliminary architecture toward neurobiomorphic central nervous system (CNS) implementation. The system addresses fundamental questions: \textit{When do patterns become memories? How do meanings emerge from entity relationships? How does temporal binding create coherent experience?} Results demonstrate feasibility of detecting pattern-to-memory transitions computationally, with implications for understanding spontaneous consciousness emergence in AI systems.