A computational modeling study of the effects of background chaotic spike trains on the signal processing of Hodgkin–Huxley neuron

In this study, we investigate how chaotic background activity affects the signal processing performance of a single Hodgkin–Huxley neuron. Chaotic fluctuations, inherent to neural systems, have been experimentally observed to influence neuronal responsiveness, particularly in the context of detecting weak periodic signals. In previous studies on chaotic resonance, signals generated directly from the Lorenz system were applied to neurons as perturbations. However, the impacts of chaotic spike fluctuations on the electrical dynamics of neurons have not been examined in the literature. Motivated by this, we employ spike trains generated from the Lorenz chaotic system to model the background activity impinging on the Hodgkin–Huxley system. A threshold parameter is introduced to systematically adjust the firing frequency of background inputs, enabling biologically realistic chaotic stimulation. Our results reveal that the presence of chaotic background inputs can lead to a resonance-like enhancement in the neuron’s ability to detect and encode weak subthreshold signals—a phenomenon known as chaotic resonance. We examine how key parameters, such as synaptic conductance and presynaptic firing rate, influence this effect. Notably, we find an inverse relationship between synaptic strength and the optimal firing frequency required to maximize signal encoding performance. Furthermore, we explore how the number of background neurons modulates this resonance, showing that information processing performance initially improves with increasing network size before saturating. These findings provide novel insights into how chaotic presynaptic activity contributes to neuronal selectivity and adaptability, suggesting that background chaos may play a critical role in intrinsic neural computation and energy-efficient signal transmission.