Electrophysiological Investigation of Insect Pain Threshold

TL;DR

This study investigates physiological responses to pain stimuli in crickets, demonstrating that insects exhibit measurable stress responses and that AI can classify pain levels with high accuracy, supporting the idea that insects can perceive pain.

Contribution

It introduces a novel AI-based method to classify insect pain states using ECG and EEG data, providing new evidence of insect nociception.

Findings

Significant heart rate and EEG changes in response to stimuli

AI model achieved 90% accuracy in pain classification

No social sharing of pain observed in ECG data

Abstract

The question of whether insects experience pain has long been debated in neuroscience and animal behavior research. Increasing evidence suggests that insects possess the ability to detect and respond to noxious stimuli, exhibiting behaviors indicative of pain perception. This study investigates the relationship between pain stimuli and physiological responses in crickets (Gryllidae), focusing on heart rate (ECG) and brain wave (EEG) patterns. We applied a range of mechanical, chemical, thermal, and electrical stimuli to crickets, recording ECG and EEG data while employing a deep learning-based model to classify pain levels. Our findings revealed significant heart rate changes and EEG fluctuations in response to various stimuli, with the highest intensity stimuli inducing marked physiological stress. The AI-based analysis, utilizing AlexNet for EEG signal classification, achieved 90% accuracy in distinguishing between resting, low-pain, and high-pain states. While no social sharing of pain was observed through ECG measurements, these results contribute to the growing body of evidence supporting insect nociception and offer new insights into their physiological responses to external stressors. This research advances the understanding of insect pain mechanisms and demonstrates the potential for AI-driven analysis in entomological studies.