RMC introduces a new computational model that transcends digital signal processing. By leveraging topological manifolds (SO(n)) and harmonic resonance, RMC provides a high-fidelity interface between neural activity and synthetic cognition.
Current neural interfaces rely on statistical filtering, which leads to data loss. RMC replaces filtering with "geometric resonance," mapping neural signals into a topological space where noise is isolated as a matter of geometric necessity.
The RMC framework utilizes a high-performance C++ kernel to handle real-time matrix operations within a reflective, closed-loop logical structure.
RMC is designed to be the foundational layer for cognitive augmentation, human-computer interaction, and autonomous neuromorphic systems, enabling machines to perform real-time logical reflection mirroring human architecture.
- Mead, C. (1990). Neuromorphic electronic systems. Proceedings of the IEEE.
- Strogatz, S. H. (2015). Nonlinear Dynamics and Chaos. Westview Press.
- Izhikevich, E. M. (2007). Dynamical Systems in Neuroscience. MIT Press.