To advance our understanding of the brain, experimental methodologies must increasingly capture biological structure and function at high resolution across a range of spatial and temporal dimensions. Now more than ever, a multi-dimensional approach is critical to allow for discovery in many areas of neuroscience, from genes and signal transduction networks to cellular connectomics and physiology to interactions between brain areas mediated by long-range neuronal projections in behaving organisms. Large-scale datasets (e.g., hundreds of terabytes) present challenges for database construction, analyses, and accessibility that will require the coordinated efforts of many research groups with diverse expertise ranging from genetics to engineering.

Experimental neuroscientists face considerable obstacles to the rapid introduction of new technologies that are essential to advance understanding, as they often do not possess the knowledge necessary to design and construct novel instrumentation. Engineers often do not understand the particular challenges of working with biological systems, impeding innovation. This duality results in unnecessary hindrance in the process of developing the next generation of tools for studying the brain. Kavli NDI eliminates these barriers by bringing together neuroscientists, engineers and theorists early and often to accelerate discovery.

To understand the exceedingly complex, multi-scale networks of the brain, renewed efforts must be made to develop rigorous theoretical models based on our ever-expanding experimental knowledge base. Creative breakthroughs in understanding brain function require extensive collaborations between biologists, engineers and theorists. At Kavli NDI, theory and computational modeling play an essential role in designing, analyzing and interpreting biological experiments and in creating new theoretical frameworks for understanding brain function.