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Low-Latency Visuotactile Neuron Using Self-Oscillating Memristor.

Pengzhan Li¹, Anping Huang¹, Jiangshun Huang¹

¹School of Physics, Beihang University, Beijing, China.

Small (Weinheim an Der Bergstrasse, Germany)

|May 25, 2026

Summary

Researchers developed a novel neuron using NbO_x memristors for ultra-low latency sensory perception. This technology enables faster, more robust multisensory integration for embodied intelligence and real-time human-machine interaction.

Keywords:

NbOx mott memristors low latency neuron multisensory perception neuromorphic computing spiking neural network

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Area of Science:

Neuromorphic Engineering
Materials Science
Artificial Intelligence

Background:

Low-latency perception is crucial for embodied intelligence and efficient human-machine interaction.
Biological systems use time-to-first-spike (TTFS) and firing rate for real-time sensory processing.
Hardware implementation of low-latency spike conversion remains a significant challenge.

Purpose of the Study:

To demonstrate a self-oscillating neuron based on NbO_x memristors for simultaneous TTFS and rate encoding.
To achieve ultra-low latency perception of visual and tactile stimuli.
To evaluate the performance of combined TTFS and rate coding for neuromorphic systems.

Main Methods:

Fabrication of a self-oscillating neuron utilizing NbO_x memristors and intrinsic parasitic capacitance.
Implementation of simultaneous TTFS and rate encoding for visual and pressure stimuli.
Evaluation using CIFAR-10 and Braille datasets for classification tasks.

Main Results:

Achieved an ultra-low first spike latency of 260 ns for visual and pressure stimuli.
Demonstrated superior accuracy, noise robustness, and reduced temporal latency compared to rate coding alone.
Validated multisensory integration for Braille recognition, showing improved accuracy in visually constrained scenarios.

Conclusions:

The NbO_x memristor-based neuron enables intrinsically low-latency multisensory perception.
Combined TTFS and rate coding offers significant advantages for neuromorphic systems.
This technology holds promise for advancing real-time human-machine interaction and embodied intelligence.