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• {"link"=>{"url"=>"http://www.citeulike.org/user/martin_pyka/article/8063122"}, "post_time"=>"2013-05-08 08:49:51", "tag"=>["neurons", "simulation"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pcbi.1000877"}, "username"=>"martin_pyka", "article_id"=>"8063122"}
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{"title"=>"One rule to grow them all: A general theory of neuronal branching and its practical application", "type"=>"journal", "authors"=>[{"first_name"=>"Hermann", "last_name"=>"Cuntz", "scopus_author_id"=>"7801443359"}, {"first_name"=>"Friedrich", "last_name"=>"Forstner", "scopus_author_id"=>"18037191000"}, {"first_name"=>"Alexander", "last_name"=>"Borst", "scopus_author_id"=>"7006608619"}, {"first_name"=>"Michael", "last_name"=>"Häusser", "scopus_author_id"=>"7003425281"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "scopus"=>"2-s2.0-78049446478", "pui"=>"359882443", "doi"=>"10.1371/journal.pcbi.1000877", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "sgr"=>"78049446478", "pmid"=>"20700495"}, "id"=>"f3a958c1-4004-39e0-8cc5-34081d0caa6e", "abstract"=>"Author Summary More than a century has passed since Ramón y Cajal presented a set of fundamental biological laws of neuronal branching. He described how the shape of the core elements of the neural circuitry – axons and dendrites – are constrained by physical parameters such as space, cytoplasmic volume, and conduction time. The existence of these laws enabled him to organize his histological observations, to formulate the neuron doctrine, and to infer directionality in signal flow in the nervous system. We show that Cajal's principles can be used computationally to generate synthetic neural circuits. These principles rigorously constrain the shape of real neuronal structures, providing direct validation of his theories. At the same time, this strategy provides us with a powerful set of tools for generating synthetic neurons, as well as a model-based approach for automated reconstructions of neuronal trees from confocal image stacks.", "link"=>"http://www.mendeley.com/research/one-rule-grow-them-general-theory-neuronal-branching-practical-application", "reader_count"=>367, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>16, "Researcher"=>106, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>124, "Student > Postgraduate"=>9, "Student > Master"=>49, "Other"=>10, "Student > Bachelor"=>24, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>2, "Professor"=>14}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>16, "Researcher"=>106, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>124, "Student > Postgraduate"=>9, "Student > Master"=>49, "Other"=>10, "Student > Bachelor"=>24, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>2, "Professor"=>14}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>171, "Arts and Humanities"=>2, "Philosophy"=>1, "Business, Management and Accounting"=>1, "Chemistry"=>1, "Computer Science"=>38, "Economics, Econometrics and Finance"=>1, "Engineering"=>20, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>8, "Medicine and Dentistry"=>16, "Neuroscience"=>57, "Physics and Astronomy"=>25, "Psychology"=>8, "Social Sciences"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>16}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>25}, "Psychology"=>{"Psychology"=>8}, "Mathematics"=>{"Mathematics"=>8}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Engineering"=>{"Engineering"=>20}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>57}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>171}, "Computer Science"=>{"Computer Science"=>38}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Argentina"=>1, "United States"=>11, "Japan"=>3, "United Kingdom"=>13, "Portugal"=>4, "Switzerland"=>1, "Russia"=>1, "Spain"=>3, "Greece"=>1, "New Zealand"=>1, "Canada"=>2, "Netherlands"=>1, "Norway"=>1, "China"=>2, "Brazil"=>1, "Denmark"=>1, "Italy"=>1, "Israel"=>1, "France"=>1, "Germany"=>19, "Estonia"=>1}, "group_count"=>9}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/417024"], "description"=>"<div><p>Understanding the principles governing axonal and dendritic branching is essential for unravelling the functionality of single neurons and the way in which they connect. Nevertheless, no formalism has yet been described which can capture the general features of neuronal branching. Here we propose such a formalism, which is derived from the expression of dendritic arborizations as locally optimized graphs. Inspired by Ramón y Cajal's laws of conservation of cytoplasm and conduction time in neural circuitry, we show that this graphical representation can be used to optimize these variables. This approach allows us to generate synthetic branching geometries which replicate morphological features of any tested neuron. The essential structure of a neuronal tree is thereby captured by the density profile of its spanning field and by a single parameter, a balancing factor weighing the costs for material and conduction time. This balancing factor determines a neuron's electrotonic compartmentalization. Additions to this rule, when required in the construction process, can be directly attributed to developmental processes or a neuron's computational role within its neural circuit. The simulations presented here are implemented in an open-source software package, the “TREES toolbox,” which provides a general set of tools for analyzing, manipulating, and generating dendritic structure, including a tool to create synthetic members of any particular cell group and an approach for a model-based supervised automatic morphological reconstruction from fluorescent image stacks. These approaches provide new insights into the constraints governing dendritic architectures. They also provide a novel framework for modelling and analyzing neuronal branching structures and for constructing realistic synthetic neural networks.</p></div>", "links"=>[], "tags"=>["neuronal", "branching"], "article_id"=>142334, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/One_Rule_to_Grow_Them_All_A_General_Theory_of_Neuronal_Branching_and_Its_Practical_Application/142334", "title"=>"One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-08-05 00:38:54"}
• {"files"=>["https://ndownloader.figshare.com/files/836779"], "description"=>"<p>(A) The tree consists of cylinders or frusta (red) connecting each two nodes along the directed edges (away from the root node, arrows). Branch points and termination points represent the topology (topological points). A branch is a set of continuation points between two topological points. The labelling of the nodes is unique following three principles: hierarchical sorting, continuous labelling preserving sub-tree consistency and topological sorting (see text). (B) Rearrangement of node locations on a sample tree. Examples of equidistant node redistribution resulting in 10 or 20 µm resampling and a 20 µm resampling including length conservation (see text and “resampling” section of <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#s4\" target=\"_blank\">Methods</a>). (C–E) Unique representations of topology and electrotonic properties from sample tree from (B). (C) Applying topological sorting, a unique electrotonic equivalent tree can be constructed by mapping node label hierarchy on the branch angle (equivalent tree). (D) The adjacency matrix depicts the connectivity between the nodes of a tree. The corresponding electrotonic signature (current transfer from a node to another, i.e. the potential difference measured in one node as a result of a current injection into another) describes the dendritic compartmentalization (see text). The electrotonic signature corresponding to the 20 µm resampled tree preserves the compartmentalization of the original tree. (E) A one-dimensional string fully describes the topology once the nodes of a tree are sorted topologically. Green pieces represent branches ending with a branch point while black pieces end with a termination point. Branch lengths correspond to real metric length and their order follows the node label sorting. Because all representations observe the same continuous labelling, they preserve the sub-tree structure (a red transparent patch highlights one such sub-tree throughout all representations in (C–E)).</p>", "links"=>[], "tags"=>["topological", "electrotonic", "neuronal"], "article_id"=>507144, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_topological_and_electrotonic_identity_of_a_neuronal_tree_/507144", "title"=>"The topological and electrotonic identity of a neuronal tree.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 01:59:04"}
• {"files"=>["https://ndownloader.figshare.com/files/836895"], "description"=>"<p>(A) The growth described by an extended minimum spanning tree algorithm (see text). Unconnected carrier points (red) are connected one by one to the nodes of a tree (black). Red dashed lines indicate three sample Euclidean distances to the nodes of the tree for sample point <i>P</i>. (B) Example trees grown on homogeneously distributed random carrier points in a circular hull starting from a root located at its centre (see top). Plotted as a function of the balancing factor <i>bf</i>, the trees range from perfect minimum spanning trees (left) to almost direct connections from the root to any point (right).</p>", "links"=>[], "tags"=>["neuronal", "branching", "structures", "optimized"], "article_id"=>507264, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generating_neuronal_branching_structures_using_optimized_graphs_/507264", "title"=>"Generating neuronal branching structures using optimized graphs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:01:04"}
• {"files"=>["https://ndownloader.figshare.com/files/837009"], "description"=>"<p>(A) Reconstruction of a starburst amacrine cell in the inner plexiform layer of the rabbit retina (data from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi.1000877-Bloomfield1\" target=\"_blank\">[24]</a>). (B) Synthetic starburst amacrine cell morphologies can be best obtained by distributing random carrier points along a density ring limited by a circular hull. (C) An example tree grown on random carrier points distributed according to B following the algorithm described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g002\" target=\"_blank\">Figure 2</a>. Spatial jitter was added to reproduce the wriggliness of the original structure. (D) A tree grown on exactly the same points as (C) with a lower balancing factor. (E) The number of randomly distributed carrier points and the balancing factor <i>bf</i> determine the synthetically generated morphology. Here, the areas are plotted in which the synthetic trees match the original according to certain criteria (blue: total cable length ±200 µm; red: total number of branch points ±5; green: mean path length to the root ±3 µm). The area of overlap corresponds to a reasonable parameter set for the synthetic trees. (F–H) Branch order distribution, path length distribution and Sholl intersections are compared for the original tree (red) and for one sample synthetic tree (grey).</p>", "links"=>[], "tags"=>["dendritic", "structures", "constructing", "geometric", "spanning", "retinal", "starburst", "amacrine"], "article_id"=>507372, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generating_dendritic_structures_by_constructing_geometric_spanning_fields_I_the_retinal_starburst_amacrine_cell_/507372", "title"=>"Generating dendritic structures by constructing geometric spanning fields: I. the retinal starburst amacrine cell.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:02:52"}
• {"files"=>["https://ndownloader.figshare.com/files/837123"], "description"=>"<p>(A) Reconstructions of four sample hippocampal granule cells (data from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi.1000877-Rihn1\" target=\"_blank\">[28]</a>). (B) After centring, rotating and scaling all cells adequately, the 50 µm iso-distance volume hulls (black lines) around the set of all topological points (black dots) overlap in all dimensions. Left, xy-projection; Middle, xz-projection; Right; yz-projection. Overlay colours represent local density with same colormap as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g003\" target=\"_blank\">Figure 3</a>. (C) Examples of synthetically generated granule cells (based on the data in AB) with <i>bf</i> = 0.85. (D) Third cell from the left in C was grown on the same carrier points with different balancing factors to show the effect of <i>bf</i> here. (E–G) Overlaid branch order distributions, path length distributions and Sholl intersections for original trees (red) and for synthetic trees with suitable parameter <i>bf</i> = 0.85 (black).</p>", "links"=>[], "tags"=>["dendritic", "structures", "constructing", "geometric", "spanning", "hippocampal", "dentate", "gyrus", "granule"], "article_id"=>507484, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generating_dendritic_structures_by_constructing_geometric_spanning_fields_II_the_hippocampal_dentate_gyrus_granule_cell_/507484", "title"=>"Generating dendritic structures by constructing geometric spanning fields: II. the hippocampal dentate gyrus granule cell.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:04:44"}
• {"files"=>["https://ndownloader.figshare.com/files/837245"], "description"=>"<p>(A) After rotating rat somatosensory cortex layer 2/3, 4 and 5 pyramidal cells to overlap, the limits of their individual regions were extracted: black shaded boxes show the mean limits in XY for the apical region; the black empty boxes delineate one standard deviation away from the mean. Corresponding red boxes duplicate this procedure for the basal dendrites. Cells are then scaled region-by-region to the mean limits of each region. Overlay colours describe local density (colormap see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g002\" target=\"_blank\">Figure 2D</a>) of lumped topological points of scaled trees. (B) Same procedure for three groups of cortical pyramidal cells during development. (C) Construction stages of a sample layer 5 pyramidal cell according to spanning fields described in A. First the apical tuft is constructed, then oblique dendrites and finally the basal dendrite. Spatial jitter and diameter values are added subsequently.</p>", "links"=>[], "tags"=>["generating", "synthetic", "cortical", "pyramidal"], "article_id"=>507611, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_general_strategy_for_generating_synthetic_morphologies_Cortical_pyramidal_cells_/507611", "title"=>"A general strategy for generating synthetic morphologies: Cortical pyramidal cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:06:51"}
• {"files"=>["https://ndownloader.figshare.com/files/837365"], "description"=>"<p>(A), (B) and (C) show sample synthetically generated model cells of layer 2/3, layer 4 and layer 5 cortical pyramidal cells respectively, all grown using the general strategy described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g005\" target=\"_blank\">Figure 5</a>. In A and B, all dendrites were thickened by 1 µm over all cells for clarity purposes. (D) When data from developing neurons was binned into three groups (P0–5, P8–12, P36–44), synthetically generated cortical pyramidal cells could be generated for the different developmental stages. Vertical or horizontal locations of the cells are purely for layout purposes in all cases.</p>", "links"=>[], "tags"=>["cells", "grown"], "article_id"=>507734, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_cells_grown_using_the_general_strategy_/507734", "title"=>"Sample cells grown using the general strategy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:08:54"}
• {"files"=>["https://ndownloader.figshare.com/files/837486"], "description"=>"<p>While branching statistics of starburst amacrine cells and hippocampal granule cells were moderately homogeneous, pyramidal cells exhibited stronger variations. Balancing factors leading to reasonable branching statistics ranged from 0.4 to 0.7. In the following we compare branching parameter distributions as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g004\" target=\"_blank\">Figure 4</a> for synthetic (black) and original dendrites (red) of layer 2/3 (A–C), layer 4 (D–F) and layer 5 (G–I) pyramidal cells grown with a balancing factor <i>bf</i> = 0.7, 0.6 and 0.5 respectively. (K) Representative layer 5 pyramidal cells grown with different balancing factors <i>bf</i> = 0, <i>bf</i> = 0.2, <i>bf</i> = 0.7. (L) Representative electrotonic signatures of these synthetically generated dendrites and of one original layer 5 pyramidal cell for comparison. (M) Simple relationship between electrotonic compartmentalization and balancing factor. Straight line connects averages of each 100 model dendrites at different balancing factor values. Dashed line shows average compartment size of real reconstructions.</p>", "links"=>[], "tags"=>["synthetic", "branching", "structures", "pyramidal"], "article_id"=>507857, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validating_synthetic_branching_structures_of_pyramidal_cells_/507857", "title"=>"Validating synthetic branching structures of pyramidal cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:10:57"}
• {"files"=>["https://ndownloader.figshare.com/files/837636"], "description"=>"<p>(A) Nine synthetic neuronal trees grown competitively on a sample square substrate of homogeneously distributed random carrier points: the competitive greedy growth results automatically in tiling of the available space. (B) Three out of 16 neuronal trees grown competitively on random carrier points distributed on a ring: this simulates well the sharp borders of Purkinje cells in the cerebellum. Whether Purkinje cell dendrites actually tile in sagittal planes of the cerebellum remains to be determined. (C) Hippocampal granule cells from <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g004\" target=\"_blank\">Figure 4</a> were scaled and positioned along the contours of a human dentate gyrus obtained from a sketch by Camillo Golgi <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi.1000877-Golgi1\" target=\"_blank\">[31]</a>. Growing synthetic CA3 hippocampal pyramidal cells competitively with the limits from the template resulted in realistic hippocampal pyramidal cells affected by mutual avoidance. Synthetic dendrites were overlaid on the background of the original sketch. (D) Bipolar cells (black) in the retina were grown competitively to connect an array of photoreceptors (yellow) to an array of starburst amacrine cells (green, obtained using the algorithm in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g003\" target=\"_blank\">Figure 3</a>). In such a case the full morphology of bipolar cells is determined by the context of the circuitry, after prescribing soma locations of the bipolar cells. For all panels of <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi-1000877-g008\" target=\"_blank\">Figure 8</a> precise scale bars would depend on the details of the preparations and were therefore omitted.</p>", "links"=>[], "tags"=>["interactions", "neuronal", "branching"], "article_id"=>508008, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_interactions_between_neuronal_branching_and_the_network_context_/508008", "title"=>"The interactions between neuronal branching and the network context.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:13:28"}
• {"files"=>["https://ndownloader.figshare.com/files/837783"], "description"=>"<p>(A) Example of an additional application of the algorithm: automated model-based tree reconstruction from image stacks. Maximum intensity projection of tiled image stacks containing a sample sub-tree of a fluorescently labelled neuronal tree. Blue overlay in top panel corresponds to the output of a non-linear thresholding. The resulting binary matrix is then reduced to single points in space (green dots) via a skeletonization procedure. After a distance graph is obtained which describes the probability of a connection between these points due to the image data the points are used as carrier points for the growth algorithm to obtain the corresponding tree using the distance graph as an additional cost factor. After unlikely branches are removed the underlying tree structure is captured (green tree structure in the lower panel, see text for more detail; note absence of scale bar since this a sample image stack). (B) Maximum intensity projections of tiled 2-photon fluorescent image stacks acquired at 820 nm from primary visual cortex of a p13 JAX transgenic mouse (strain #007677, <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000877#pcbi.1000877-Chattopadhyaya1\" target=\"_blank\">[38]</a>) expressing GFP in parvalbumin interneurons, of which one is present. Three further layer 5 pyramidal neurons are also imaged; all cells were filled with a fluorescent dye Alexa 594 via whole cell patch-clamp recording. Data courtesy of Kate Buchanan and Jesper Sjöström. (C) Corresponding reconstructions (with the interneuron in green) grown in a competitive manner on the image stacks after manual post-processing.</p>", "links"=>[], "tags"=>["reconstruction", "cells", "greedy"], "article_id"=>508152, "categories"=>["Neuroscience", "Developmental Biology", "Medicine", "Biophysics"], "users"=>["Hermann Cuntz", "Friedrich Forstner", "Alexander Borst", "Michael Häusser"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000877.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automated_reconstruction_of_multiple_cells_using_the_greedy_algorithm_/508152", "title"=>"Automated reconstruction of multiple cells using the greedy algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-05 02:15:52"}

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Relative Metric

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F1000Prime | Further Information

• {"year"=>2014, "month"=>5, "doi"=>"10.1371/journal.pcbi.1000877", "f1000_id"=>"6784956", "url"=>"http://f1000.com/prime/6784956", "score"=>1, "classifications"=>[], "updated_at"=>"2014-05-26T08:32:50Z"}
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• {"year"=>2014, "month"=>10, "doi"=>"10.1371/journal.pcbi.1000877", "f1000_id"=>"6784956", "url"=>"http://f1000.com/prime/6784956", "score"=>1, "classifications"=>[], "updated_at"=>"2014-10-27T08:32:45Z"}
• {"year"=>2014, "month"=>11, "doi"=>"10.1371/journal.pcbi.1000877", "f1000_id"=>"6784956", "url"=>"http://f1000.com/prime/6784956", "score"=>1, "classifications"=>[], "updated_at"=>"2014-11-24T09:31:49Z"}
• {"year"=>2014, "month"=>12, "doi"=>"10.1371/journal.pcbi.1000877", "f1000_id"=>"6784956", "url"=>"http://f1000.com/prime/6784956", "score"=>1, "classifications"=>[], "updated_at"=>"2014-12-29T09:41:38Z"}
• {"year"=>2015, "month"=>1, "doi"=>"10.1371/journal.pcbi.1000877", "f1000_id"=>"6784956", "url"=>"http://f1000.com/prime/6784956", "score"=>1, "classifications"=>[], "updated_at"=>"2015-01-26T09:40:38Z"}
• {"year"=>2015, "month"=>2, "doi"=>"10.1371/journal.pcbi.1000877", "f1000_id"=>"6784956", "url"=>"http://f1000.com/prime/6784956", "score"=>1, "classifications"=>[], "updated_at"=>"2015-02-23T09:40:38Z"}
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