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4 Janelia Publications

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    Magee LabChklovskii Lab
    12/01/09 | Experience-dependent compartmentalized dendritic plasticity in rat hippocampal CA1 pyramidal neurons.
    Makara JK, Losonczy A, Wen Q, Magee JC
    Nature Neuroscience. 2009 Dec;12(12):1485-7. doi: 10.1038/nn.2428

    The excitability of individual dendritic branches is a plastic property of neurons. We found that experience in an enriched environment increased propagation of dendritic Na(+) spikes in a subset of individual dendritic branches in rat hippocampal CA1 pyramidal neurons and that this effect was mainly mediated by localized downregulation of A-type K(+) channel function. Thus, dendritic plasticity might be used to store recent experience in individual branches of the dendritic arbor.

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    Chklovskii Lab
    07/28/09 | Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors.
    Wen Q, Stepanyants A, Elston GN, Grosberg AY, Chklovskii DB
    Proceedings of the National Academy of Sciences of the United States of America. 2009 Jul 28;106(30):12536-41. doi: 10.1371/journal.pcbi.1001066

    The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function.

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    Chklovskii Lab
    01/30/09 | Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs.
    Mishchenko Y
    Journal of Neuroscience Methods. 2009 Jan 30;176(2):276-89. doi: 10.1016/j.jneumeth.2008.09.006

    We describe an approach for automation of the process of reconstruction of neural tissue from serial section transmission electron micrographs. Such reconstructions require 3D segmentation of individual neuronal processes (axons and dendrites) performed in densely packed neuropil. We first detect neuronal cell profiles in each image in a stack of serial micrographs with multi-scale ridge detector. Short breaks in detected boundaries are interpolated using anisotropic contour completion formulated in fuzzy-logic framework. Detected profiles from adjacent sections are linked together based on cues such as shape similarity and image texture. Thus obtained 3D segmentation is validated by human operators in computer-guided proofreading process. Our approach makes possible reconstructions of neural tissue at final rate of about 5 microm3/manh, as determined primarily by the speed of proofreading. To date we have applied this approach to reconstruct few blocks of neural tissue from different regions of rat brain totaling over 1000microm3, and used these to evaluate reconstruction speed, quality, error rates, and presence of ambiguous locations in neuropil ssTEM imaging data.

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    Chklovskii Lab
    01/01/09 | Reconstruction of sparse circuits using multi-neuronal excitation (RESCUME).
    Hu T, Chklovskii DB
    Neural Information Processing Systems. 2009;22:790-8

    One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially pre-synaptic neurons while monitoring the membrane voltage of the post-synaptic neuron. Reconstructing a large neural circuit using such a "brute force" approach is rather time-consuming and inefficient because the connectivity in neural circuits is sparse. Instead, we propose to measure a post-synaptic neuron's voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. To reconstruct these synaptic connections from the recorded voltage we apply a decoding algorithm recently developed for compressive sensing. Compared to the brute force approach, our method promises significant time savings that grow with the size of the circuit. We use computer simulations to find optimal stimulation parameters and explore the feasibility of our reconstruction method under realistic experimental conditions including noise and non-linear synaptic integration. Multineuronal stimulation allows reconstructing synaptic connectivity just from the spiking activity of post-synaptic neurons, even when sub-threshold voltage is unavailable. By using calcium indicators, voltage-sensitive dyes, or multi-electrode arrays one could monitor activity of multiple postsynaptic neurons simultaneously, thus mapping their synaptic inputs in parallel, potentially reconstructing a complete neural circuit.

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