The brain is composed of networks of thousands or millions of neurons, and modern neuroscience is just reaching the stage where researchers can observe most, if not all, of these neurons in living animals. Yet, many of the most fundamental questions in neuroscience still remain unanswered. How does the brain represent sensory information? How do responses to sensory information change when paired with good or bad outcomes? How do the brain’s sensory and motor systems change during learning? One obstacle to answering these questions is that measurements of the activity of every neuron in the brain generate massive, complex datasets that researchers are only now learning to interpret. We have positioned our team at the forefront of these efforts to capture and interpret such complicated neural data. We are developing an open-source computing library for interpreting the large-scale data produced by whole-brain recordings in larval zebra fish. These computing strategies will incorporate cutting-edge technology from disparate fields such as machine learning, data mining, and distributed computing. In addition, this combination of experimental data and computational analysis provides a platform for fruitful collaboration between experimentalists and theorists. Our work will be particularly attractive to theoretical neuroscientists because brain simulations can be directly compared to the data: Since the recordings are from the entire brain, there is no need to make assumptions about neuronal activity. Such a partnership promises to bridge the gap not just between theory and experiments, but also to provide fundamental insights into the brain’s biology.
Misha Ahrens, Howard Hughes Medical Institute, Janelia Farm Research Campus
Larry Abbott, Columbia University
John Cunningham, Columbia University
Jeremy Freeman, Howard Hughes Medical Institute, Janelia Farm Research Campus
Liam Paninski, Columbia University