A Comprehensive Model of External and Internal Interference in Neural Communication Systems for Enhanced IoNT Performance

Neural communication systems utilize human neurons to transmit data among biological entities and engineered nanodevices, forming the Internet of Nanothings (IoNT). As a novel research topic, the interference with neural communication systems is not well investigated and modeled. In this article, a simplified neural communication model with external and internal interference is proposed. In our model, external interference from neighboring neurons is characterized by a Poisson distribution, capturing the stochastic nature of interference spikes. Internal interference, attributed to relative refractoriness, which is a neural property reflecting the reduced probability of spike generation shortly after neuron activation, is modeled using a dynamic threshold. The simulation results show that although the only internal interference scenario causes minimal, it still introduces some extra symbol error when external and internal interference both exist. Enhancing the intensity of neural signal stimulation and lengthening the signal period are effective strategies for mitigating the effects of external and internal interference, respectively. The work contributes to a better understanding of neural communication systems and paves the way for the future potential applications of healthcare.