Search Results - "Chklovskii, Dmitri B."

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    Neuronal Circuits Underlying Persistent Representations Despite Time Varying Activity by Druckmann, Shaul, Chklovskii, Dmitri B.

    Published in Current biology (20-11-2012)
    “…Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory…”
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    Structural properties of the Caenorhabditis elegans neuronal network by Varshney, Lav R, Chen, Beth L, Paniagua, Eric, Hall, David H, Chklovskii, Dmitri B

    Published in PLoS computational biology (03-02-2011)
    “…Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into…”
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  3. 3

    Machine learning of hierarchical clustering to segment 2D and 3D images by Nunez-Iglesias, Juan, Kennedy, Ryan, Parag, Toufiq, Shi, Jianbo, Chklovskii, Dmitri B

    Published in PloS one (20-08-2013)
    “…We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing…”
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    CaImAn an open source tool for scalable calcium imaging data analysis by Giovannucci, Andrea, Friedrich, Johannes, Gunn, Pat, Kalfon, Jérémie, Brown, Brandon L, Koay, Sue Ann, Taxidis, Jiannis, Najafi, Farzaneh, Gauthier, Jeffrey L, Zhou, Pengcheng, Khakh, Baljit S, Tank, David W, Chklovskii, Dmitri B, Pnevmatikakis, Eftychios A

    Published in eLife (17-01-2019)
    “…Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible…”
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    A linear discriminant analysis model of imbalanced associative learning in the mushroom body compartment by Lipshutz, David, Kashalikar, Aneesh, Farashahi, Shiva, Chklovskii, Dmitri B

    Published in PLoS computational biology (06-02-2023)
    “…To adapt to their environments, animals learn associations between sensory stimuli and unconditioned stimuli. In invertebrates, olfactory associative learning…”
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    Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning by Qin, Shanshan, Farashahi, Shiva, Lipshutz, David, Sengupta, Anirvan M., Chklovskii, Dmitri B., Pehlevan, Cengiz

    Published in Nature neuroscience (01-02-2023)
    “…Recent experiments have revealed that neural population codes in many brain areas continuously change even when animals have fully learned and stably perform…”
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    Wiring Economy and Volume Exclusion Determine Neuronal Placement in the Drosophila Brain by Rivera-Alba, Marta, Vitaladevuni, Shiv N., Mishchenko, Yuriy, Lu, Zhiyuan, Takemura, Shin-ya, Scheffer, Lou, Meinertzhagen, Ian A., Chklovskii, Dmitri B., de Polavieja, Gonzalo G.

    Published in Current biology (06-12-2011)
    “…Wiring economy has successfully explained the individual placement of neurons in simple nervous systems like that of Caenorhabditis elegans [1–3] and the…”
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    The comprehensive connectome of a neural substrate for 'ON' motion detection in Drosophila by Takemura, Shin-Ya, Nern, Aljoscha, Chklovskii, Dmitri B, Scheffer, Louis K, Rubin, Gerald M, Meinertzhagen, Ian A

    Published in eLife (22-04-2017)
    “…Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in…”
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    Wiring Optimization Can Relate Neuronal Structure and Function by Chen, Beth L., Hall, David H., Chklovskii, Dmitri B.

    “…We pursue the hypothesis that neuronal placement in animals minimizes wiring costs for given functional constraints, as specified by synaptic connectivity…”
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    Semi-automated reconstruction of neural circuits using electron microscopy by Chklovskii, Dmitri B, Vitaladevuni, Shiv, Scheffer, Louis K

    Published in Current opinion in neurobiology (01-10-2010)
    “…Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously…”
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  14. 14

    Ultrastructural Analysis of Hippocampal Neuropil from the Connectomics Perspective by Mishchenko, Yuriy, Hu, Tao, Spacek, Josef, Mendenhall, John, Harris, Kristen M., Chklovskii, Dmitri B.

    Published in Neuron (Cambridge, Mass.) (23-09-2010)
    “…Complete reconstructions of vertebrate neuronal circuits on the synaptic level require new approaches. Here, serial section transmission electron microscopy…”
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    Highly nonrandom features of synaptic connectivity in local cortical circuits by Song, Sen, Sjöström, Per Jesper, Reigl, Markus, Nelson, Sacha, Chklovskii, Dmitri B

    Published in PLoS biology (01-03-2005)
    “…How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous…”
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    A Cost-Benefit Analysis of Neuronal Morphology by Wen, Quan, Chklovskii, Dmitri B

    Published in Journal of neurophysiology (01-05-2008)
    “…1 Cold Spring Harbor Laboratory, Cold Spring Harbor; and 2 Department of Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, New…”
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    Optimal Information Storage in Noisy Synapses under Resource Constraints by Varshney, Lav R., Sjöström, Per Jesper, Chklovskii, Dmitri B.

    Published in Neuron (Cambridge, Mass.) (09-11-2006)
    “…Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. We propose a theoretical framework that…”
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    Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors by Wen, Quan, Stepanyants, Armen, Elston, Guy N, Grosberg, Alexander Y, Chklovskii, Dmitri B

    “…The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we…”
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    Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit by Bharioke, Arjun, Chklovskii, Dmitri B

    Published in PLoS computational biology (01-08-2015)
    “…Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this…”
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