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 - 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 - 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 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D 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 -