This study presents a high-order sensory processing nanocircuit based on coupled VO₂ oscillators, demonstrating a novel approach for efficient and compact neuromorphic sensory systems. The system combines a capacitance-coupled VO₂ phase-change oscillatory network with a decision-making module for post-processing, enabling touch and gesture recognition with fewer devices and lower energy-delay-product compared to conventional methods. The VO₂ oscillators exhibit high cycle-to-cycle uniformity and can reach oscillation frequencies up to 2.6 MHz. Three coupled oscillators can encode eight synchronization modes with a low EDP of 3.07 pJs. The system leverages the rich internal dynamics of volatile memristors to convert sensory information into periodic spiking activities, mimicking biological neural systems. The oscillatory network encodes information in phase differences, while the decision-making module processes this information for classification. The system demonstrates significant advantages in area and power efficiency, with a delay time of about 20 μs and a low EDP of 3.07 pJs, which is 1000 times lower than classic CMOS methods. The study also shows that the system can be extended to other sensory inputs by replacing capacitive sensors with appropriate resistive sensors. The results highlight the potential of the proposed system for efficient sensory processing tasks, with the ability to interact with the environment and perform complex computations. The work opens new avenues for neuromorphic computing, leveraging the unique properties of VO₂ oscillators for high-order dynamics and nonlinear processing.This study presents a high-order sensory processing nanocircuit based on coupled VO₂ oscillators, demonstrating a novel approach for efficient and compact neuromorphic sensory systems. The system combines a capacitance-coupled VO₂ phase-change oscillatory network with a decision-making module for post-processing, enabling touch and gesture recognition with fewer devices and lower energy-delay-product compared to conventional methods. The VO₂ oscillators exhibit high cycle-to-cycle uniformity and can reach oscillation frequencies up to 2.6 MHz. Three coupled oscillators can encode eight synchronization modes with a low EDP of 3.07 pJs. The system leverages the rich internal dynamics of volatile memristors to convert sensory information into periodic spiking activities, mimicking biological neural systems. The oscillatory network encodes information in phase differences, while the decision-making module processes this information for classification. The system demonstrates significant advantages in area and power efficiency, with a delay time of about 20 μs and a low EDP of 3.07 pJs, which is 1000 times lower than classic CMOS methods. The study also shows that the system can be extended to other sensory inputs by replacing capacitive sensors with appropriate resistive sensors. The results highlight the potential of the proposed system for efficient sensory processing tasks, with the ability to interact with the environment and perform complex computations. The work opens new avenues for neuromorphic computing, leveraging the unique properties of VO₂ oscillators for high-order dynamics and nonlinear processing.