Response mechanism of heat-sensitive neurons under combined noise stimulation

Patients with congenital analgesia who lack the ability to sense temperature generally face low survival rates, highlighting a critical need to understand the underlying mechanisms of heat sensation. While previous research has focused on modeling neural responses to stimuli, the specific mechanisms by which heat-sensitive neurons respond to external temperature changes remain unclear. This gap in knowledge is particularly relevant, as identifying how these neurons react to diverse stimuli can provide insight into sensory deficits linked to congenital analgesia. In this study, we developed a model of heat-sensitive neurons based on the FitzHugh-Nagumo (FHN) neural circuit to investigate neuronal response patterns to external heat stimuli. Two distinct stimulus patterns, each combined with Gaussian white noise, were applied to the model to induce varied firing modes. By calculating the Hamilton energy for each firing mode, we quantified the impact of each external stimulus on neuronal activity. A correlation function was further defined to explore how different stimuli influence the selection of firing modes. Simulation results demonstrate that heat-sensitive neurons show a preferential response to stimuli that induce spike discharge over stimuli that induce r-clonic patterns, as seen in changes to the periodic attractor contours. When exposed to Chua's circuit stimulus, chaotic emission patterns reveal significant shifts in attractor contour, indicating a strong response to spike, r-clonic, and periodic stimuli. These findings suggest that external stimuli capable of inducing spike-and-wave or r-clonic patterns are sensitively detected by thermosensitive neurons, leading to heightened Hamilton energy release and increased regularity in neural activity. This study enhances our understanding of thermosensitive neuronal dynamics under complex stimuli, shedding light on potential response mechanisms relevant to sensory dysfunction in congenital analgesia and advancing the broader field of neural response modeling.