Constraint-Based Photonic Neuromorphic Computing: Implementing Biological Neural Dynamics Through Geometric Holonomic Constraints

This paper introduces a paradigm shift in neuromorphic engineering by transitioning from software-based neural simulations to the physical embodiment of biological constraints. By mapping intracellular and intercellular limitations (such as metabolic energy balance, protein trafficking delays, and homeostatic regulation) onto geometric holonomic constraints within a photonic architecture, the authors create a system where biological realism is enforced by the laws of physics. Using Optical Geometric Logic Units (GLUs), the architecture represents neurons as complex organelle networks rather than point-processors. This framework not only offers a path toward highly efficient, brain-principled AI but also provides a novel platform for computational pathology, where neurodegenerative diseases like Alzheimer’s and Parkinson’s are modeled as deliberate geometric constraint violations.