Integrated optical studies in larger brains exacerbate the “big data” problem, which is already becoming a notable challenge in multiple subareas of neuroscience. Collaborations between neuroscientists and computer scientists will become increasingly important, and even essential, for the challenges of the next 25 years—not only for generating testable hypotheses arising from models of brain dynamics or machine learning research, but also for storing, handling,

processing, and making accessible these vast data streams selleck screening library concurrent with the emergence of integrated and computational optical approaches. For example, large-scale Ca2+ recordings in mice will come to produce gigabytes per second of data, while CLARITY data sets for individual whole rodent brains can be ∼1–10 terabytes in size, depending on the number of color channels (Figures 1 and 3). These optical data sets will soon grow to GDC-0449 manufacturer the ∼10 petabyte scale

and beyond, especially when larger brains including those of humans are examined at high resolution. However, conventional “cloud storage” approaches for large data sets are in many ways suboptimal for the kinds of data encountered in neuroscience, and computational/analytical methods will have to be profoundly accelerated simply to keep pace with the exhilarating new rate of data acquisition in neuroscience. Lastly, we close with some remarks on how engineers and neuroscientists might fruitfully interact in the coming years. Traditionally, there often Sitaxentan have not been conventional career paths, at least in academics, for engineers playing critical supporting roles in neuroscience research. In many cases, engineering departments might not view such activity as breaking sufficient ground in the engineering realm, whereas

biology departments might not appreciate the crucial but underlying links to biological discovery. As the engineering challenges become increasingly severe for neuroscientists in the years ahead, with an upcoming deluge of sophisticated instrumentation and massive data sets, the neuroscience community will need to consider carefully how best to engage and retain the best, brightest, and most ambitious engineers. Both the engineering and neuroscience communities might be well served by further appreciation of each other’s intellectual traditions and modus operandi. Engineers are typically motivated to address wide sets of problems that share central features, permitting common tools and approaches. Biologists are usually motivated to solve specific mysteries in detail. These are distinct intellectual mind sets, and the two communities can sometimes talk past each other.