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28 Janelia Publications

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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
    11/02/17 | Network-size independent covering number bounds for deep networks.
    Kabra M, Branson KM
    arXiv. 2017 Nov 02:arXiv:1711.00753

    We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn't affect the covering number. Thus, we can ignore the rotation part of each layer's linear transformation, and get the covering number bound by concentrating on the scaling part.

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    07/13/17 | Mapping the neural substrates of behavior.
    Robie AA, Hirokawa J, Edwards AW, Umayam LA, Lee A, Phillips ML, Card GM, Korff W, Rubin GM, Simpson JH, Reiser MB, Branson KM
    Cell. 2017-07-13;170(2):393-406. doi: 10.1016/j.cell.2017.06.032

    Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

    •We developed machine-vision methods to broadly and precisely quantify fly behavior•We measured effects of activating 2,204 genetically targeted neuronal populations•We created whole-brain maps of neural substrates of locomotor and social behaviors•We created resources for exploring our results and enabling further investigation

    Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

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    05/22/17 | Spatial Memory: Mice Quickly Learn a Safe Haven.
    Egnor SE
    Current Biology : CB. 2017 May 22;27(10):R388-R390. doi: 10.1016/j.cub.2017.04.007

    New work on innate escape behavior shows that mice spontaneously form a spatially precise memory of the location of shelter, which is laid down quickly and updated continuously.

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    04/01/17 | Time-accuracy tradeoffs in kernel prediction: controlling prediction quality.
    Kpotufe S, Verma N
    Journal of Machine Learning Research. 2017 Apr 1 ;18(44):1-29

    Kernel regression or classification (also referred to as weighted ε-NN methods in Machine Learning) are appealing for their simplicity and therefore ubiquitous in data analysis. How- ever, practical implementations of kernel regression or classification consist of quantizing or sub-sampling data for improving time efficiency, often at the cost of prediction quality. While such tradeoffs are necessary in practice, their statistical implications are generally not well understood, hence practical implementations come with few performance guaran- tees. In particular, it is unclear whether it is possible to maintain the statistical accuracy of kernel prediction—crucial in some applications—while improving prediction time.

    The present work provides guiding principles for combining kernel prediction with data- quantization so as to guarantee good tradeoffs between prediction time and accuracy, and in particular so as to approximately maintain the good accuracy of vanilla kernel prediction.

    Furthermore, our tradeoff guarantees are worked out explicitly in terms of a tuning parameter which acts as a knob that favors either time or accuracy depending on practical needs. On one end of the knob, prediction time is of the same order as that of single-nearest- neighbor prediction (which is statistically inconsistent) while maintaining consistency; on the other end of the knob, the prediction risk is nearly minimax-optimal (in terms of the original data size) while still reducing time complexity. The analysis thus reveals the interaction between the data-quantization approach and the kernel prediction method, and most importantly gives explicit control of the tradeoff to the practitioner rather than fixing the tradeoff in advance or leaving it opaque.

    The theoretical results are validated on data from a range of real-world application domains; in particular we demonstrate that the theoretical knob performs as expected. 

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    03/06/17 | Moonwalker Descending Neurons Mediate Visually Evoked Retreat in Drosophila.
    Sen R, Wu M, Branson K, Robie A, Rubin GM, Dickson BJ
    Current Biology : CB. 2017 Mar 6;27(5):766-71. doi: 10.1016/j.cub.2017.02.008

    Insects, like most animals, tend to steer away from imminent threats [1-7]. Drosophila melanogaster, for example, generally initiate an escape take-off in response to a looming visual stimulus, mimicking a potential predator [8]. The escape response to a visual threat is, however, flexible [9-12] and can alternatively consist of walking backward away from the perceived threat [11], which may be a more effective response to ambush predators such as nymphal praying mantids [7]. Flexibility in escape behavior may also add an element of unpredictability that makes it difficult for predators to anticipate or learn the prey's likely response [3-6]. Whereas the fly's escape jump has been well studied [8, 9, 13-18], the neuronal underpinnings of evasive walking remain largely unexplored. We previously reported the identification of a cluster of descending neurons-the moonwalker descending neurons (MDNs)-the activity of which is necessary and sufficient to trigger backward walking [19], as well as a population of visual projection neurons-the lobula columnar 16 (LC16) cells-that respond to looming visual stimuli and elicit backward walking and turning [11]. Given the similarity of their activation phenotypes, we hypothesized that LC16 neurons induce backward walking via MDNs and that turning while walking backward might reflect asymmetric activation of the left and right MDNs. Here, we present data from functional imaging, behavioral epistasis, and unilateral activation experiments that support these hypotheses. We conclude that LC16 and MDNs are critical components of the neural circuit that transduces threatening visual stimuli into directional locomotor output.

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    02/08/17 | Cilia-mediated Hedgehog signaling controls form and function in the mammalian larynx.
    Tabler JM, Rigney MM, Berman GJ, Gopalakrishnan S, Heude E, Al-Lami HA, Yannakoudakis BZ, Fitch RD, Carter CM, Vokes SA, Liu KJ, Tajbakhsh S, Egnor SR, Wallingford JB
    eLife. 2017 Feb 08;6:. doi: 10.7554/eLife.19153

    Acoustic communication is fundamental to social interactions among animals, including humans. In fact, deficits in voice impair the quality of life for a large and diverse population of patients. Understanding the molecular genetic mechanisms of development and function in the vocal apparatus is thus an important challenge with relevance both to the basic biology of animal communication and to biomedicine. However, surprisingly little is known about the developmental biology of the mammalian larynx. Here, we used genetic fate mapping to chart the embryological origins of the tissues in the mouse larynx, and we describe the developmental etiology of laryngeal defects in mice with disruptions in cilia-mediated Hedgehog signaling. In addition, we show that mild laryngeal defects correlate with changes in the acoustic structure of vocalizations. Together, these data provide key new insights in the molecular genetics of form and function in the mammalian vocal apparatus.

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    01/01/17 | Machine vision methods for analyzing social interactions.
    Robie AA, Seagraves KM, Egnor SE, Branson K
    The Journal of Experimental Biology. 2017 Jan 01;220(Pt 1):25-34. doi: 10.1242/jeb.142281

    Recent developments in machine vision methods for automatic, quantitative analysis of social behavior have immensely improved both the scale and level of resolution with which we can dissect interactions between members of the same species. In this paper, we review these methods, with a particular focus on how biologists can apply them to their own work. We discuss several components of machine vision-based analyses: methods to record high-quality video for automated analyses, video-based tracking algorithms for estimating the positions of interacting animals, and machine learning methods for recognizing patterns of interactions. These methods are extremely general in their applicability, and we review a subset of successful applications of them to biological questions in several model systems with very different types of social behaviors.

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    12/13/16 | An empirical analysis of deep network loss surfaces.
    Im DJ, Tao M, Branson K
    arXiv. 2016 Dec 13:arXiv:1612.04010

    The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find minima.

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    11/01/16 | Learning recurrent representations for hierarchical behavior modeling.
    Eyjolfsdottir E, Branson K, Yue Y, Perona P
    arXiv. 2016 Nov 1;arXiv:1611.00094(arXiv:1611.00094):

    We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules.

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