On a circuit level, neurons code information based on their state and environmental context. Dr. Friedman’s lab goal is to develop an understanding of their governing principles, similar to the laws of other fields of science, such as physics and chemistry, in which distinct rules can be used to predict the outcome of a system. Knowledge of these principles will help to construct a basis for the development of biologically-informed treatment for addiction, PTSD, and a range of other psychiatric disorders. Also, this knowledge could inform artificial intelligence to develop natural intelligence based algorithms, which may result in new AI horizons. For example, flies and mice can learn to navigate in a complex environment and make better and more efficient choices than the best algorithms running on powerful computers. Friedman lab using its dual strength in computation and circuit physiology to formulate these principles and generalize them to be useful by AI. Even though these goals are ambitious, steps toward their accomplishment will be important for brain-physiology and AI communities. Also, there is a crucial need for biologically-informed, targeted treatments for a range of psychiatric disorders, including addiction and PTSD. Modern technology allows for simultaneous recording of hundreds of neurons, critical for identification of the mechanisms of behavior dysfunction. However, computational tools for analysis of simultaneously recorded big data sets have not yet emerged. Using our dual background in behavioral circuit physiology and computer science, we combine an experimental and computational approach to examine the role of the basal ganglia based circuit in addiction, stress, depression, and PTSD and develop computational tools that elucidate abnormal circuit signal transduction.