We study how neurons and neural circuits encode sensory information and how these processes change in pathological pain. Our work combines in vitro and in vivo approaches, allowing us to investigate the nervous system from the activity and physiology of individual cells to the dynamics of defined neural circuits. Using electrophysiology, calcium imaging, and cell-type-specific manipulations, we aim to understand how sensory information is processed across these levels and how altered circuit function contributes to pain and related behavioral states.

Pain is ultimately expressed through behavior, and careful measurement of behavior is essential for understanding how animals experience and respond to sensory states. We use quantitative behavioral assays together with continuous video analysis to study pain-related behaviors, sleep, arousal, movement, and other behavioral states. By linking behavior to neural activity, we aim to understand how changes in specific circuits shape the way an organism responds to pain and other internal states.

Neural activity and behavior are complex and highly dynamic, requiring quantitative approaches to reveal patterns that may not be apparent by observation alone. We use computational modeling to analyze neural and behavioral data, identify relationships across multiple levels of organization, and generate testable predictions about circuit function. These models help us connect activity in individual neurons and populations to changes in behavior and perception.

We develop and apply AI-based tools to extract meaningful information from complex experimental data. A major focus is high-resolution behavioral analysis, including automated identification of behavioral patterns and discovery of previously unrecognized behavioral states. We also use machine-learning approaches to integrate neural and behavioral measurements, allowing us to uncover relationships that can guide new experiments and refine our understanding of pain-related circuits.