An Enhanced Neural Communication Model for IoNT Based on the Oscillatory Characteristics of Membrane Potential

The Internet of Nanothings (IoNT) enables in-body communication, but transmitting signals to external devices remains a key challenge. Neural communication provides a promising interface, yet existing models often oversimplify membrane potentials as binary states, ignoring their subthreshold oscillatory dynamics. To address this, we propose a biologically realistic neural communication model that incorporates the resonate-and-fire (RF) neuron model, capturing the damped oscillations in membrane potential. Accordingly, we design two coding and modulation schemes: enhanced dual-pulse on-off keying (EDP-OOK), which aligns pulse intervals with the neuron’s oscillatory period for optimal excitation or suppression, and tunable dual-pulse on-off keying (TDP-OOK), which flexibly adjusts pulse intensity for energy-efficient suppression. The transmission efficiency is evaluated using the bit error rate (BER). Simulation results show the proposed schemes achieve reliable transmission with lower power consumption compared to conventional methods. This research opens up possibilities for efficiently connecting IoNT to external networks.