Low-Latency Visuotactile Neuron Using Self-Oscillating Memristor

Low latency perception of visual and tactile information is a prerequisite for embodied intelligence, and combined with bio-inspired spiking neural network (SNN), it enables efficient interaction with the physical environment. However, achieving low-latency perception and spike conversion of external stimuli at the hardware level remains challenging, while biological systems utilize the time-to-first-spike (TTFS) and firing rate of neurons to perceive surrounding signals in real time. Herein, a self-oscillating neuron based on NbO_x memristors is demonstrated, leveraging intrinsic parasitic capacitance as the sole integration element to enable simultaneous TTFS and rate encoding of visual and pressure stimuli with an ultra-low first spike latency of 260 ns. By eliminating redundant capacitance and exploiting the adaptive timing characteristics of TTFS encoding, an intrinsically low-latency signal transmission pathway is established. Evaluation on the CIFAR-10 dataset demonstrates that the fusion of TTFS and rate coding achieves higher accuracy, improved noise robustness, and reduced temporal latency compared to rate coding schemes. Furthermore, multisensory integration is validated through Braille datasets recognition, demonstrating improved accuracy under visually constrained conditions. These results highlight the potential of multisensory neuromorphic perception systems for real time human-machine interaction and embodied intelligence applications.