TY - GEN A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Broser, Philip J. A1 - Bruno, Randy M. A1 - Sakmann, Bert T1 - NeuroMorph and NeuroCount: Automated tools for fast and objective acquisition of neuronal morphology for quantitative structural analysis T2 - Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics Y1 - 2008 U6 - https://doi.org/10.3389/conf.neuro.11.2008.01.065 ER - TY - GEN A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Lang, Stefan A1 - Sakmann, Bert T1 - 3D mapping of synaptic connections within “in silico” microcircuits of full compartmental neurons in extended networks on the example of VPM axons projecting into S1 of rats. T2 - Conference Abstract, Neuroinformatics Y1 - 2009 U6 - https://doi.org/10.3389/conf.neuro.11.2009.08.092 ER - TY - GEN A1 - Oberlaender, Marcel A1 - Bruno, Randy M. A1 - de Kock, Christiaan P. J. A1 - Meyer, Hanno A1 - Dercksen, Vincent J. A1 - Sakmann, Bert T1 - 3D distribution and sub-cellular organization of thalamocortical VPM synapses for individual excitatory neuronal cell types in rat barrel cortex T2 - Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN) Y1 - 2009 ER - TY - CHAP A1 - Dercksen, Vincent J. A1 - Weber, Britta A1 - Günther, David A1 - Oberlaender, Marcel A1 - Prohaska, Steffen A1 - Hege, Hans-Christian T1 - Automatic alignment of stacks of filament data T2 - Proc. IEEE International Symposium on Biomedical Imaging Y1 - 2009 SP - 971 EP - 974 PB - IEEE press CY - Boston, USA ER - TY - JOUR A1 - Oberlaender, Marcel A1 - Dercksen, Vincent J. A1 - Egger, Robert A1 - Gensel, Maria A1 - Sakmann, Bert A1 - Hege, Hans-Christian T1 - Automated three-dimensional detection and counting of neuron somata JF - Journal of Neuroscience Methods Y1 - 2009 U6 - https://doi.org/10.1016/j.jneumeth.2009.03.008 VL - 180 IS - 1 SP - 147 EP - 160 ER - TY - JOUR A1 - Kleinfeld, David A1 - Bharioke, Arjun A1 - Blinder, Pablo A1 - Bock, David A1 - Briggman, Kevin A1 - Chklovskii, Dmitri A1 - Denk, Winfried A1 - Helmstaedter, Moritz A1 - Kaufhold, John A1 - Lee, Wei-Chung A1 - Meyer, Hanno A1 - Micheva, Kristina A1 - Oberlaender, Marcel A1 - Prohaska, Steffen A1 - Reid, R. A1 - Smith, Stephen A1 - Takemura, Shinya A1 - Tsai, Philbert A1 - Sakmann, Bert T1 - Large-scale automated histology in the pursuit of connectomes JF - Journal of Neuroscience Y1 - 2011 UR - http://www.zib.de/prohaska/docs/Kleinfeld_JNS_Connectomes_2011.pdf U6 - https://doi.org/10.1523/JNEUROSCI.4077-11.2011 VL - 31 IS - 45 SP - 16125 EP - 16138 ER - TY - JOUR A1 - Lang, Stefan A1 - Dercksen, Vincent J. A1 - Sakmann, Bert A1 - Oberlaender, Marcel T1 - Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex JF - Neural Networks Y1 - 2011 U6 - https://doi.org/doi:10.1016/j.neunet.2011.06.013 VL - 24 IS - 9 SP - 998 EP - 1011 ER - TY - GEN A1 - Dercksen, Vincent J. A1 - Oberlaender, Marcel A1 - Sakmann, Bert A1 - Hege, Hans-Christian T1 - Light Microscopy-Based Reconstruction and Interactive Structural Analysis of Cortical Neural Networks T2 - BioVis 2011 Abstracts, 1st IEEE Symposium on Biological Data Visualization Y1 - 2011 ER - TY - GEN A1 - Dercksen, Vincent J. A1 - Oberlaender, Marcel A1 - Sakmann, Bert A1 - Hege, Hans-Christian ED - Linsen, Lars ED - Hagen, Hans ED - Hamann, Bernd ED - Hege, Hans-Christian T1 - Interactive Visualization – a Key Prerequisite for Reconstruction of Anatomically Realistic Neural Networks T2 - Visualization in Medicine and Life Sciences II Y1 - 2012 UR - http://www.zib.de/visual/publications/sources/src-2012/DercksenVMLS2012web.pdf SP - 27 EP - 44 PB - Springer, Berlin ER - TY - CHAP A1 - Dercksen, Vincent J. A1 - Egger, Robert A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis T2 - Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) Y1 - 2012 U6 - https://doi.org/10.2312/VCBM/VCBM12/017-024 SP - 17 EP - 24 CY - Norrköping, Sweden ER - TY - JOUR A1 - Oberlaender, Marcel A1 - de Kock, Christiaan P. J. A1 - Bruno, Randy M. A1 - Ramirez, Alejandro A1 - Meyer, Hanno A1 - Dercksen, Vincent J. A1 - Helmstaedter, Moritz A1 - Sakmann, Bert T1 - Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex JF - Cerebral Cortex Y1 - 2012 U6 - https://doi.org/doi:10.1093/cercor/bhr317 VL - 22 IS - 10 SP - 2375 EP - 2391 ER - TY - GEN A1 - Dercksen, Vincent J. A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology N2 - Neuroanatomical analysis, such as classification of cell types, depends on reliable reconstruction of large numbers of complete 3D dendrite and axon morphologies. At present, the majority of neuron reconstructions are obtained from preparations in a single tissue slice in vitro, thus suffering from cut off dendrites and, more dramatically, cut off axons. In general, axons can innervate volumes of several cubic millimeters and may reach path lengths of tens of centimeters. Thus, their complete reconstruction requires in vivo labeling, histological sectioning and imaging of large fields of view. Unfortunately, anisotropic background conditions across such large tissue volumes, as well as faintly labeled thin neurites, result in incomplete or erroneous automated tracings and even lead experts to make annotation errors during manual reconstructions. Consequently, tracing reliability renders the major bottleneck for reconstructing complete 3D neuron morphologies. Here, we present a novel set of tools, integrated into a software environment named ‘Filament Editor’, for creating reliable neuron tracings from sparsely labeled in vivo datasets. The Filament Editor allows for simultaneous visualization of complex neuronal tracings and image data in a 3D viewer, proof-editing of neuronal tracings, alignment and interconnection across sections, and morphometric analysis in relation to 3D anatomical reference structures. We illustrate the functionality of the Filament Editor on the example of in vivo labeled axons and demonstrate that for the exemplary dataset the final tracing results after proof-editing are independent of the expertise of the human operator. T3 - ZIB-Report - 13-75 Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-43157 SN - 1438-0064 ER - TY - JOUR A1 - Dercksen, Vincent J. A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology JF - NeuroInformatics Y1 - 2014 U6 - https://doi.org/10.1007/s12021-013-9213-2 VL - 12 IS - 2 SP - 325 EP - 339 PB - Springer US ER - TY - GEN A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Kock, Christiaan P.J. A1 - Oberlaender, Marcel ED - Cuntz, Hermann ED - Remme, Michiel W.H. ED - Torben-Nielsen, Benjamin T1 - Reverse Engineering the 3D Structure and Sensory-Evoked Signal Flow of Rat Vibrissal Cortex T2 - The Computing Dendrite Y1 - 2014 U6 - https://doi.org/10.1007/978-1-4614-8094-5_8 VL - 11 SP - 127 EP - 145 PB - Springer CY - New York ER - TY - JOUR A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Udvary, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Generation of dense statistical connectomes from sparse morphological data JF - Frontiers in Neuroanatomy Y1 - 2014 U6 - https://doi.org/10.3389/fnana.2014.00129 VL - 8 IS - 129 ER - TY - GEN A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Udvary, Daniel A1 - Hege, Hans-Christian A1 - Oberlaender, Marcel T1 - Generation of dense statistical connectomes from sparse morphological data N2 - Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and sub-cellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results. T3 - ZIB-Report - 14-43 KW - 3D neural network KW - Dense connectome KW - Reconstruction Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-53075 SN - 1438-0064 ER - TY - JOUR A1 - Landau, Itamar D. A1 - Egger, Robert A1 - Dercksen, Vincent J. A1 - Oberlaender, Marcel A1 - Sompolinsky, Haim T1 - The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks JF - Neuron N2 - Models of cortical dynamics often assume a homogeneous connectivity structure. However, we show that heterogeneous input connectivity can prevent the dynamic balance between excitation and inhibition, a hallmark of cortical dynamics, and yield unrealistically sparse and temporally regular firing. Anatomically based estimates of the connectivity of layer 4 (L4) rat barrel cortex and numerical simulations of this circuit indicate that the local network possesses substantial heterogeneity in input connectivity, sufficient to disrupt excitation-inhibition balance. We show that homeostatic plasticity in inhibitory synapses can align the functional connectivity to compensate for structural heterogeneity. Alternatively, spike-frequency adaptation can give rise to a novel state in which local firing rates adjust dynamically so that adaptation currents and synaptic inputs are balanced. This theory is supported by simulations of L4 barrel cortex during spontaneous and stimulus-evoked conditions. Our study shows how synaptic and cellular mechanisms yield fluctuation-driven dynamics despite structural heterogeneity in cortical circuits. Y1 - 2016 U6 - https://doi.org/10.1016/j.neuron.2016.10.027 VL - 92 IS - 5 SP - 1106 EP - 1121 ER - TY - JOUR A1 - Udvary, Daniel A1 - Harth, Philipp A1 - Macke, Jakob H. A1 - Hege, Hans-Christian A1 - de Kock, Christiaan P. J. A1 - Sakmann, Bert A1 - Oberlaender, Marcel T1 - A Theory for the Emergence of Neocortical Network Architecture JF - BioRxiv Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1101/2020.11.13.381087 ER - TY - CHAP A1 - Harth, Philipp A1 - Vohra, Sumit A1 - Udvary, Daniel A1 - Oberlaender, Marcel A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - A Stratification Matrix Viewer for Analysis of Neural Network Data T2 - Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) N2 - The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex. Y1 - 2022 U6 - https://doi.org/10.2312/vcbm.20221194 CY - Vienna, Austria ER - TY - JOUR A1 - Udvary, Daniel A1 - Harth, Philipp A1 - Macke, Jakob H. A1 - Hege, Hans-Christian A1 - de Kock, Christiaan P. J. A1 - Sakmann, Bert A1 - Oberlaender, Marcel T1 - The Impact of Neuron Morphology on Cortical Network Architecture JF - Cell Reports N2 - The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density and cellular diversity of the neuropil. The higher these three factors are the more recurrent is the topology of the network. Conversely, the lower these factors are the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular and network scales from the specific morphological properties of its neuronal constituents. Y1 - 2022 U6 - https://doi.org/10.1016/j.celrep.2022.110677 VL - 39 IS - 2 ER -