Spinal-inspired artificial tactile interneuron with high-order burst spiking for intelligent edge interfaces

Human tactile perception relies on hierarchical processing, where inputs entering the nervous system are fused by interneurons for sparse multimodal encoding, and the integrated signals are sent to the brain to generate perception. Replicating this pathway from primary sensory inputs to higher-order neural processing, which efficiently transforms signals into coherent representations of the external environment, is essential for artificial tactile systems. Here we present artificial multimodal interneuron (AMINs) by integrating strain, pressure, and temperature sensors with NbO _x memristor neurons on an all-in-one platform, enabling hierarchical neural encoding and the generation of high-information-density temporal spike patterns. AMIN-based processing generates a unified burst spikes with wide temporal dynamics that encode object size, hardness, and temperature, serving directly as input to SNNs. In a 20-class tactile object recognition task, the AMINs-SNN system achieves 90.5% accuracy with robust and low-redundancy encoding, paving the way for compact, low-power multimodal tactile intelligent systems.