@article{EggerDercksenUdvaryetal., author = {Egger, Robert and Dercksen, Vincent J. and Udvary, Daniel and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Generation of dense statistical connectomes from sparse morphological data}, series = {Frontiers in Neuroanatomy}, volume = {8}, journal = {Frontiers in Neuroanatomy}, number = {129}, doi = {10.3389/fnana.2014.00129}, language = {en} } @article{LangDercksenSakmannetal.2011, author = {Lang, Stefan and Dercksen, Vincent J. and Sakmann, Bert and Oberlaender, Marcel}, title = {Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex}, series = {Neural Networks}, volume = {24}, journal = {Neural Networks}, number = {9}, doi = {doi:10.1016/j.neunet.2011.06.013}, pages = {998 -- 1011}, year = {2011}, language = {en} } @misc{DercksenOberlaenderSakmannetal.2011, author = {Dercksen, Vincent J. and Oberlaender, Marcel and Sakmann, Bert and Hege, Hans-Christian}, title = {Light Microscopy-Based Reconstruction and Interactive Structural Analysis of Cortical Neural Networks}, series = {BioVis 2011 Abstracts, 1st IEEE Symposium on Biological Data Visualization}, journal = {BioVis 2011 Abstracts, 1st IEEE Symposium on Biological Data Visualization}, year = {2011}, language = {en} } @inproceedings{DercksenEggerHegeetal.2012, author = {Dercksen, Vincent J. and Egger, Robert and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis}, series = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, address = {Norrk{\"o}ping, Sweden}, doi = {10.2312/VCBM/VCBM12/017-024}, pages = {17 -- 24}, year = {2012}, language = {en} } @article{OberlaenderdeKockBrunoetal.2012, author = {Oberlaender, Marcel and de Kock, Christiaan P. J. and Bruno, Randy M. and Ramirez, Alejandro and Meyer, Hanno and Dercksen, Vincent J. and Helmstaedter, Moritz and Sakmann, Bert}, title = {Cell Type-Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex}, series = {Cerebral Cortex}, volume = {22}, journal = {Cerebral Cortex}, number = {10}, doi = {doi:10.1093/cercor/bhr317}, pages = {2375 -- 2391}, year = {2012}, language = {en} } @article{KleinfeldBhariokeBlinderetal.2011, author = {Kleinfeld, David and Bharioke, Arjun and Blinder, Pablo and Bock, David and Briggman, Kevin and Chklovskii, Dmitri and Denk, Winfried and Helmstaedter, Moritz and Kaufhold, John and Lee, Wei-Chung and Meyer, Hanno and Micheva, Kristina and Oberlaender, Marcel and Prohaska, Steffen and Reid, R. and Smith, Stephen and Takemura, Shinya and Tsai, Philbert and Sakmann, Bert}, title = {Large-scale automated histology in the pursuit of connectomes}, series = {Journal of Neuroscience}, volume = {31}, journal = {Journal of Neuroscience}, number = {45}, doi = {10.1523/JNEUROSCI.4077-11.2011}, pages = {16125 -- 16138}, year = {2011}, language = {en} } @article{OberlaenderDercksenEggeretal.2009, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Egger, Robert and Gensel, Maria and Sakmann, Bert and Hege, Hans-Christian}, title = {Automated three-dimensional detection and counting of neuron somata}, series = {Journal of Neuroscience Methods}, volume = {180}, journal = {Journal of Neuroscience Methods}, number = {1}, doi = {10.1016/j.jneumeth.2009.03.008}, pages = {147 -- 160}, year = {2009}, language = {en} } @misc{OberlaenderDercksenLangetal.2009, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Lang, Stefan and Sakmann, Bert}, title = {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.}, series = {Conference Abstract, Neuroinformatics}, journal = {Conference Abstract, Neuroinformatics}, doi = {10.3389/conf.neuro.11.2009.08.092}, year = {2009}, language = {en} } @misc{OberlaenderBrunodeKocketal.2009, author = {Oberlaender, Marcel and Bruno, Randy M. and de Kock, Christiaan P. J. and Meyer, Hanno and Dercksen, Vincent J. and Sakmann, Bert}, title = {3D distribution and sub-cellular organization of thalamocortical VPM synapses for individual excitatory neuronal cell types in rat barrel cortex}, series = {Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN)}, journal = {Conference Abstract No. 173.19/Y35, 39th Annual Meeting of the Society for Neuroscience (SfN)}, year = {2009}, language = {en} } @article{LandauEggerDercksenetal., author = {Landau, Itamar D. and Egger, Robert and Dercksen, Vincent J. and Oberlaender, Marcel and Sompolinsky, Haim}, title = {The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks}, series = {Neuron}, volume = {92}, journal = {Neuron}, number = {5}, doi = {10.1016/j.neuron.2016.10.027}, pages = {1106 -- 1121}, abstract = {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.}, language = {en} } @article{DercksenHegeOberlaender2014, author = {Dercksen, Vincent J. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology}, series = {NeuroInformatics}, volume = {12}, journal = {NeuroInformatics}, number = {2}, publisher = {Springer US}, doi = {10.1007/s12021-013-9213-2}, pages = {325 -- 339}, year = {2014}, language = {en} } @misc{EggerDercksenKocketal.2014, author = {Egger, Robert and Dercksen, Vincent J. and Kock, Christiaan P.J. and Oberlaender, Marcel}, title = {Reverse Engineering the 3D Structure and Sensory-Evoked Signal Flow of Rat Vibrissal Cortex}, series = {The Computing Dendrite}, volume = {11}, journal = {The Computing Dendrite}, editor = {Cuntz, Hermann and Remme, Michiel W.H. and Torben-Nielsen, Benjamin}, publisher = {Springer}, address = {New York}, doi = {10.1007/978-1-4614-8094-5_8}, pages = {127 -- 145}, year = {2014}, language = {en} } @misc{OberlaenderDercksenBroseretal.2008, author = {Oberlaender, Marcel and Dercksen, Vincent J. and Broser, Philip J. and Bruno, Randy M. and Sakmann, Bert}, title = {NeuroMorph and NeuroCount: Automated tools for fast and objective acquisition of neuronal morphology for quantitative structural analysis}, series = {Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics}, journal = {Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics}, doi = {10.3389/conf.neuro.11.2008.01.065}, year = {2008}, language = {en} } @inproceedings{DercksenWeberGuentheretal.2009, author = {Dercksen, Vincent J. and Weber, Britta and G{\"u}nther, David and Oberlaender, Marcel and Prohaska, Steffen and Hege, Hans-Christian}, title = {Automatic alignment of stacks of filament data}, series = {Proc. IEEE International Symposium on Biomedical Imaging}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging}, publisher = {IEEE press}, address = {Boston, USA}, pages = {971 -- 974}, year = {2009}, language = {en} } @misc{EggerDercksenUdvaryetal., author = {Egger, Robert and Dercksen, Vincent J. and Udvary, Daniel and Hege, Hans-Christian and Oberlaender, Marcel}, title = {Generation of dense statistical connectomes from sparse morphological data}, issn = {1438-0064}, doi = {10.3389/fnana.2014.00129}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-53075}, abstract = {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.}, language = {en} } @misc{DercksenHegeOberlaender2013, author = {Dercksen, Vincent J. and Hege, Hans-Christian and Oberlaender, Marcel}, title = {The Filament Editor: An Interactive Software Environment for Visualization, Proof-Editing and Analysis of 3D Neuron Morphology}, issn = {1438-0064}, doi = {10.1007/s12021-013-9213-2}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-43157}, year = {2013}, abstract = {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.}, language = {en} } @article{UdvaryHarthMackeetal., author = {Udvary, Daniel and Harth, Philipp and Macke, Jakob H. and Hege, Hans-Christian and de Kock, Christiaan P. J. and Sakmann, Bert and Oberlaender, Marcel}, title = {The Impact of Neuron Morphology on Cortical Network Architecture}, series = {Cell Reports}, volume = {39}, journal = {Cell Reports}, number = {2}, doi = {10.1016/j.celrep.2022.110677}, abstract = {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.}, language = {en} } @inproceedings{HarthVohraUdvaryetal., author = {Harth, Philipp and Vohra, Sumit and Udvary, Daniel and Oberlaender, Marcel and Hege, Hans-Christian and Baum, Daniel}, title = {A Stratification Matrix Viewer for Analysis of Neural Network Data}, series = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, address = {Vienna, Austria}, doi = {10.2312/vcbm.20221194}, abstract = {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.}, language = {en} } @misc{DercksenOberlaenderSakmannetal.2012, author = {Dercksen, Vincent J. and Oberlaender, Marcel and Sakmann, Bert and Hege, Hans-Christian}, title = {Interactive Visualization - a Key Prerequisite for Reconstruction of Anatomically Realistic Neural Networks}, series = {Visualization in Medicine and Life Sciences II}, journal = {Visualization in Medicine and Life Sciences II}, editor = {Linsen, Lars and Hagen, Hans and Hamann, Bernd and Hege, Hans-Christian}, publisher = {Springer, Berlin}, pages = {27 -- 44}, year = {2012}, language = {en} } @article{UdvaryHarthMackeetal., author = {Udvary, Daniel and Harth, Philipp and Macke, Jakob H. and Hege, Hans-Christian and de Kock, Christiaan P. J. and Sakmann, Bert and Oberlaender, Marcel}, title = {A Theory for the Emergence of Neocortical Network Architecture}, series = {BioRxiv}, journal = {BioRxiv}, doi = {https://doi.org/10.1101/2020.11.13.381087}, language = {en} } @article{BoeltsHarthGaoetal., author = {Boelts, Jan and Harth, Philipp and Gao, Richard and Udvary, Daniel and Yanez, Felipe and Baum, Daniel and Hege, Hans-Christian and Oberlaender, Marcel and Macke, Jakob H.}, title = {Simulation-based inference for efficient identification of generative models in computational connectomics}, series = {PLOS Computational Biology}, volume = {19}, journal = {PLOS Computational Biology}, number = {9}, doi = {10.1371/journal.pcbi.1011406}, abstract = {Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.}, language = {en} } @article{BoeltsHarthGaoetal., author = {Boelts, Jan and Harth, Philipp and Gao, Richard and Udvary, Daniel and Yanez, Felipe and Baum, Daniel and Hege, Hans-Christian and Oberlaender, Marcel and Macke, Jakob H}, title = {Simulation-based inference for efficient identification of generative models in connectomics}, series = {bioRxiv}, journal = {bioRxiv}, doi = {10.1101/2023.01.31.526269}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-89890}, abstract = {Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neural networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a single rule to fit the empirical data, SBI considers many parametrizations of a wiring rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rules and relies on machine learning methods to estimate a probability distribution (the `posterior distribution over rule parameters conditioned on the data') that characterizes all data-compatible rules. We demonstrate how to apply SBI in connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.}, language = {en} } @inproceedings{HarthBastTroidletal., author = {Harth, Philipp and Bast, Arco and Troidl, Jakob and Meulemeester, Bjorge and Pfister, Hanspeter and Beyer, Johanna and Oberlaender, Marcel and Hege, Hans-Christian and Baum, Daniel}, title = {Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach}, series = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, doi = {10.2312/vcbm.20231218}, abstract = {Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research.}, language = {en} }