Spike timing encodes object shape better than floating-point vectors

The Thousand Brains Theory models how brains recognize objects through touch by moving a sensor across surfaces. Current implementations use dense floating-point vectors that lose information about *when* features are encountered—a critical detail for spatial reasoning. This work replaces those vectors with rank-order spike packets, where neuron firing time implicitly encodes both sensor movement and feature sequence. On synthetic benchmarks, temporal coding achieves perfect accuracy on objects with identical features in different layouts, where dense methods fail completely, and maintains a 30–50 percentage point advantage under noise.