Show HN: Stream-based AI with neurological multi-gate (Na⁺/θ/NMDA) (github.com)

A new implementation has emerged for the research paper titled "Intelligence within Bounds: Why Cognition Requires a Closed Convex Hull," which introduces Clock-Selected Compression Theory (CSCT). This framework proposes a novel understanding of how discrete symbolic representations are formed from the continuous dynamics of neural signals through geometric constraints and phase-locked timing. Essential axioms underpin the theory, including the idea that cognition relies on continuous streams and that discrete events arise from temporally aligned selections, implying a significant shift in how we perceive the relationship between neural activities and cognitive symbols. The significance of CSCT lies in its innovative approach to semantic grounding and feature binding through continuous dynamics rather than explicit symbolic concatenation. The research indicates that representing concepts requires membership within a convex hull, achieving a remarkable 96.7% success rate in semantic grounding tasks compared to a mere 16.7% in less structured settings. This framework not only enhances our understanding of cognitive processes but also holds practical implications for advancements in AI systems, where reliable symbol emergence from continuous inputs could improve machine learning models' efficiency and interpretability. The official implementation is available in Python, utilizing PyTorch for efficient compute performance.