Abstract Bridging the gap between sensory input and behavioral output remains a central challenge in neuroscience. In Drosophila, the seemingly simple behavior of heat avoidance reflects a surprisingly intricate interplay between sensory processing and rapid decision-making, one that can only be fully understood through precise stimulus control and high-resolution behavioral analysis. Here, we present an integrated platform combining a precision-controlled thermal arena with deep learning–based tracking and a suite of statistical, machine-learning, and behavioral modeling tools to quantify fine-grained locomotor dynamics across thermal gradients. Wild-type flies exhibited robust temperature-dependent strategies, including increased avoidance indices and U-turn frequencies at higher temperatures. Antennal thermoreceptor ablation disrupted rapid decision-making at thermal boundaries while preserving baseline heat avoidance, suggesting the presence of parallel thermosensory pathways. Flies with antennal impairments showed 9% higher active avoidance within hot zones, significant reductions in U-turn responses, increased traversal speeds, weakened centrophobism, and staccato movement patterns that remained unaffected. Supervised classifiers achieved 76.7% accuracy in distinguishing temperature conditions and 83.0% accuracy in identifying sensory impairments. A Braitenberg vehicle model replicated key behavioral patterns, but its reduced adaptability to abrupt changes in sensory input underscored the superior compensatory capacity of biological systems. These findings offer unprecedented quantitative resolution into Drosophila’s thermotaxis behavior and highlight the distinct contributions of peripheral and internal thermoreceptors to navigational decision-making. This scalable framework advances the study of sensorimotor integration and has potential applications in bio-inspired navigation design.
Moradi et al. (Fri,) studied this question.