<p>The rich spiking dynamics of memristors make them essential components for simulating biological neurons. This study explores the potential of vanadium dioxide (VO₂) memristive neurons for encoding and processing information through synchronous spiking activity, using numerical simulations to characterize their behavior. The neurons, organized into uncoupled groups, exhibit synchronous activity driven solely by external stimuli, overcoming desynchronization effects of intrinsic noise under sufficient input intensity. The encoding capability is analyzed through coherence functions derived from spectral analysis and correlation calculations. Results reveal that neurons encode input signals predominantly in the 0–10 MHz frequency range, with a distinct coherence peak at approximately 2.5 MHz. The linear causal relationship between synchronous outputs and inputs weakens as the neuron group size increases or input intensity decreases. By leveraging numerical methods to model stochastic dynamics and analyze coherence functions, this work highlights the significance of computational tools in unveiling complex synchronous encoding mechanisms. These findings offer a theoretical foundation for designing memristor-based intelligent sensory systems, providing insights into neuromorphic engineering and the development of energy-efficient, biologically inspired sensory nodes.</p>