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7 Publications

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    Sternson LabScheffer Lab
    12/04/14 | A genetically specified connectomics approach applied to long-range feeding regulatory circuits.
    Atasoy D, Betley JN, Li W, Su HH, Sertel SM, Scheffer LK, Simpson JH, Fetter RD, Sternson SM
    Nature Neuroscience. 2014 Dec;17(12):1830-9. doi: 10.1038/nn.3854

    Synaptic connectivity and molecular composition provide a blueprint for information processing in neural circuits. Detailed structural analysis of neural circuits requires nanometer resolution, which can be obtained with serial-section electron microscopy. However, this technique remains challenging for reconstructing molecularly defined synapses. We used a genetically encoded synaptic marker for electron microscopy (GESEM) based on intra-vesicular generation of electron-dense labeling in axonal boutons. This approach allowed the identification of synapses from Cre recombinase-expressing or GAL4-expressing neurons in the mouse and fly with excellent preservation of ultrastructure. We applied this tool to visualize long-range connectivity of AGRP and POMC neurons in the mouse, two molecularly defined hypothalamic populations that are important for feeding behavior. Combining selective ultrastructural reconstruction of neuropil with functional and viral circuit mapping, we characterized some basic features of circuit organization for axon projections of these cell types. Our findings demonstrate that GESEM labeling enables long-range connectomics with molecularly defined cell types.

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    09/05/14 | Annotating synapses in large EM datasets.
    Plaza SM, Parag T, Huang G, Olbris DJ, Saunders MA, Rivlin PK
    arXiv. 2014 Sep 5:arXiv:1409.1801 [q-bio.QM]

    Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders.

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    06/06/14 | Small sample learning of superpixel classifiers for EM segmentation- extended version.
    Parag T, Plaza SM, Scheffer LK
    arXiv. 2014 Jun 6:arXiv:1406.1774 [cs.CV]

    Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

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    06/05/14 | A context-aware delayed agglomeration framework for EM segmentation.
    Parag T, Chakraborty A, Plaza SM
    arXiv. 2014 Jun 5:arXiv:1406.1476 [cs.CV]

    This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.

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    04/04/14 | Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages
    Nunez-Iglesias J, Kennedy R, Plaza SM, Chakraborty A, William T. Katz
    Frontiers in Neuroinformatics. 2014 Apr 4;8:34. doi: 10.3389/fninf.2014.00034

    The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

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    03/02/14 | Toward large-scale connectome reconstructions.
    Plaza SM, Scheffer LK, Chklovskii DB
    Current Opinion in Neurobiology. 2014 Mar 2;25C:201-10. doi: 10.1016/j.conb.2014.01.019

    Recent results have shown the possibility of both reconstructing connectomes of small but biologically interesting circuits and extracting from these connectomes insights into their function. However, these reconstructions were heroic proof-of-concept experiments, requiring person-months of effort per neuron reconstructed, and will not scale to larger circuits, much less the brains of entire animals. In this paper we examine what will be required to generate and use substantially larger connectomes, finding five areas that need increased attention: firstly, imaging better suited to automatic reconstruction, with excellent z-resolution; secondly, automatic detection, validation, and measurement of synapses; thirdly, reconstruction methods that keep and use uncertainty metrics for every object, from initial images, through segmentation, reconstruction, and connectome queries; fourthly, processes that are fully incremental, so that the connectome may be used before it is fully complete; and finally, better tools for analysis of connectomes, once they are obtained.

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    01/20/14 | Lessons from the neurons themselves.
    Scheffer L
    Design Automation Conference (ASP-DAC), 2014 19th Asia and South Pacific. 2014 Jan 20-23:197-200. doi: 10.1109/ASPDAC.2014.6742889

    Natural neural circuits, optimized by millions of years of evolution, are fast, low power, robust, and adapt in response to experience, all characteristics we would love to have in systems we ourselves design. Recently there have been enormous advances in understanding how neurons implement computations within the brain of living creatures. Can we use this new-found knowledge to create better artificial system? What lessons can we learn from the neurons themselves, that can help us create better neuromorphic circuits?

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