Inflammatory pain represents a complex state involving sensitization of peripheral and central neuronal signaling. Resolving this high-dimensional interplay at the cellular and behavioral level is key to effective therapeutic development. Here, using the carrageenan model of local inflammation of the hind paw, we determine how carrageenan alters both the physiological state of sensory neurons and behaviors at rapid and continuous timescales. We identify higher excitability of sensory neurons innervating the site of inflammation by profiling their physiological state at different time points. To identify millisecond-resolved sensory-reflexive signatures evoked by inflammatory pain, we used a combination of supervised and unsupervised algorithms, and uncovered abnormal paw placement as a defining behavioral feature. For long-term detection and characterization of spontaneous behavioral signatures representative of affective-motivational pain states, we use computer vision coupled to unsupervised machine learning in an open arena. Using the non-steroidal anti-inflammatory drug meloxicam to characterize analgesic states during rapid and ongoing timescales, we identify a return to pre-injury states of some sensory-reflexive behaviors, but by and large, many spontaneous, affective-motivational pain behaviors remain unaffected. Taken together, this comprehensive exploration across cellular and behavioral dimensions reveals peripheral versus centrally mediated pain signatures that define the inflamed state, providing a framework for scaling the pain experience at unprecedented resolution.

For decades, advanced behavioral tasks have only been used in human and non-human primates. However, with improved analytical and genetic techniques, there has been a growing drive to implement complex reaching, decision-making, and reaction time tasksnot in primates -but in rodents. Here, we assess the hypothesis that a mouse can learn a cued reaction time task. Moreover, we tested multiple training regimens and found that introducing elements of the reaction time task serially hindered, rather than helped task acquisition. Additionally, we include a step-by-step manual for inexpensive implementation and use of a rodent joystick for behavioral analysis. Task and analysis code for the evaluated behaviors are included such that they may be replicated and tested further. With these, we also include code for a probabilistic reward 'two-arm bandit' task. These various tasks, and the method to construct and implement them, will enable greatly improved study of the neural correlates of behavior in the powerful mouse model organism. In summary, we have tested and demonstrated that mice can learn sophisticated tasks with A joystick, and that targeted task design provides a significant advantage. These results of this study stand to inform the implementation of other sophisticated tasks using the mouse model.