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4 Janelia Publications

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    Freeman Lab
    08/26/16 | Improving data quality in neuronal population recordings.
    Harris KD, Quiroga RQ, Freeman J, Smith SL
    Nature Neuroscience. 2016 Aug 26;19(9):1165-74. doi: 10.1038/nn.4365

    Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.

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    Ji LabFreeman Lab
    08/26/16 | Technologies for imaging neural activity in large volumes.
    Ji N, Freeman J, Smith SL
    Nature Neuroscience. 2016 Aug 26;19(9):1154-64. doi: 10.1038/nn.4358

    Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.

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    Freeman Lab
    04/01/16 | MLlib: Machine learning in Apache Spark.
    Meng X, Bradley J, Yavus B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai DB, Amde M, Owen S, Xin D, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A
    Journal of Machine Learning Research. 2016 Apr 01;17:1-7

    Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark’s rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed. 

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    Freeman LabAhrens Lab
    03/22/16 | Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion.
    Dunn TW, Mu Y, Narayan S, Randlett O, Naumann EA, Yang C, Schier AF, Freeman J, Engert F, Ahrens MB
    eLife. 2016 Mar 22:. doi: 10.7554/eLife.12741

    In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.

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