Project Summary New technologies have enabled amazing access to neural processes at multiple resolutions of time and space. However, the data analyses necessary to answer the scientific questions often depend on computational techniques that are unique to the experiment and may have to be modified (or even developed anew) for each specific experiment. These are not techniques that can be learned in a single class, but rather ways of thinking about problems that must be incorporated into each experiment and each analysis. Fundamentally, the data are being collected, but the field is not getting the full value of the collected data; students need additional training in order to successfully extract the complete information present in their experiments. Students need to understand both the complexities and limitations within experimental paradigms and also the complexities and limitations within computational analyses that can be applied to those paradigms. More importantly, if a student is going to develop his or her own analyses, the student needs a deep understanding of how to define and derive the appropriate control analyses. Ensuring the rigor of the experimental design and the subsequent analyses is particularly difficult for these types of data. We propose to build a comprehensive training program for both predoctoral graduate students and early-stage postdocs, to teach them how to integrate computational analyses and techniques to achieve scientific breakthroughs in neuroscience. This training will place these students in a very strong position for furthering their scientific careers. Furthermore, the training program will also help support, maintain, and improve the strong interdisciplinary community in computational, experimental, and clinical neuroscience that already exists within the University of Minnesota.