Abstract
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 NbOx memristor neurons on a hybrid integrated platform, enabling hierarchical neural encoding and the generation of high-information-density temporal spike patterns. AMIN-based processing generates a unified burst spike train 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, a hardware-characterized AMIN encoding model combined with a software SNN achieves 90.5% accuracy, demonstrating the potential of the proposed tactile encoding strategy for compact and low-power multimodal tactile intelligence.