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5 Janelia Publications

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    12/14/18 | Motor cortex is an input-driven dynamical system controlling dexterous movement.
    Sauerbrei B, Guo J, Mischiati M, Guo W, Kabra M, Verma N, Branson KM, Hantman AW
    bioRxiv. 2018-12-14:266320. doi: 10.1101/266320

    Skillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

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    10/18/18 | In toto imaging and reconstruction of post-implantation mouse development at the single-cell level.
    McDole K, Guignard L, Amat F, Berger A, Malandain G, Royer LA, Turaga SC, Branson K, Keller PJ
    Cell. 2018 Oct 10;175(3):859-876. doi: 10.1016/j.cell.2018.09.031

    The mouse embryo has long been central to the study of mammalian development; however, elucidating the cell behaviors governing gastrulation and the formation of tissues and organs remains a fundamental challenge. A major obstacle is the lack of live imaging and image analysis technologies capable of systematically following cellular dynamics across the developing embryo. We developed a light-sheet microscope that adapts itself to the dramatic changes in size, shape, and optical properties of the post-implantation mouse embryo and captures its development from gastrulation to early organogenesis at the cellular level. We furthermore developed a computational framework for reconstructing long-term cell tracks, cell divisions, dynamic fate maps, and maps of tissue morphogenesis across the entire embryo. By jointly analyzing cellular dynamics in multiple embryos registered in space and time, we built a dynamic atlas of post-implantation mouse development that, together with our microscopy and computational methods, is provided as a resource.

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    08/20/18 | Multiple animals tracking in video using part affinity fields
    Rodriguez IF, Megret R, Egnor R, Branson K, Agosto JL, Giray T, Acuna E
    Visual observation and analysis of Vertebrate And Insect Behavior 2018. 2018 Aug 20:

    In this work, we address the problem of pose detection and tracking of multiple individuals for the study of behaviour in insects and animals. Using a Deep Neural Network architecture, precise detection and association of the body parts can be performed. The models are learned based on user-annotated training videos, which gives flexibility to the approach. This is illustrated on two different animals: honeybees and mice, where very good performance in part recognition and association are observed despite the presence of multiple interacting individuals.

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    06/26/18 | Honeybee detection and pose estimation using convolutional neural networks.
    Rodriguez IF, Branson KM, Acuna E, Agosto-Rivera J, Giray T, Megret R
    RFIAP 2018. 2018 Jun 26:

    The ability to automatize the analysis of video for monitoring animals and insects is of great interest for behavior science and ecology [1]. In particular, honeybees play a crucial role in agriculture as natural pollinators. However, recent studies has shown that phenomena such as colony collapse disorder are causing the loss of many colonies [2]. Due to the high number of interacting factors to explain these events, a multi-faceted analysis of the bees in their environment is required. We focus in our work in developing tools to help model and understand their behavior as individuals, in relation with the health and performance of the colony.

    In this paper, we report the development of a new system for the detection, locali- zation and tracking of honeybee body parts from video on the entrance ramp of the colony. The proposed system builds on the recent advances in Convolutional Neu- ral Networks (CNN) for Human pose estimation and evaluates the suitability for the detection of honeybee pose as shown in Figure 1. This opens the door for novel animal behavior analysis systems that take advantage of the precise detection and tracking of the insect pose. 

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    04/03/18 | A deep (learning) dive into a cell.
    Branson K
    Nature Methods. 2018 Apr 03;15(4):253-4. doi: 10.1038/nmeth.4658