Training-Free Neuromorphic Computing Emerges from Symbolic Constraints

A novel neuromorphic computing approach generates brain-like computational behaviors directly from declarative constraint rules, eliminating the need for neural networks and training. The Universal Constraint Engine demonstrates that complex computational behaviors—memory, logic, oscillation—can emerge from minimal symbolic rule sets and be deployed across diverse hardware substrates. This symbolic approach promises greater interpretability and flexibility than traditional neural network-based neuromorphic systems.