@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}, 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} } @article{LandauEggerDercksenetal.2016, 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}, volume = {92}, journal = {Neuron}, number = {5}, doi = {10.1016/j.neuron.2016.10.027}, pages = {1106 -- 1121}, year = {2016}, 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} } @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}, 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{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}, volume = {180}, journal = {Journal of Neuroscience Methods}, number = {1}, doi = {10.1016/j.jneumeth.2009.03.008}, pages = {147 -- 160}, year = {2009}, language = {en} } @article{EggerDercksenUdvaryetal.2014, 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}, volume = {8}, journal = {Frontiers in Neuroanatomy}, number = {129}, doi = {10.3389/fnana.2014.00129}, year = {2014}, language = {en} } @misc{EggerDercksenUdvaryetal.2014, 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}, year = {2014}, 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} } @article{XieGruberCrampenetal.2025, author = {Xie, Kunpeng and Gruber, Lennart Johannes and Crampen, Martin and Li, Yao and Ferreira, Andr{\´e} and Tappeiner, Elias and Gillot, Maxime and Schepers, Jan and Xu, Jiangchang and Pankert, Tobias and Beyer, Michel and Shahamiri, Negar and ten Brink, Reinier and Dot, Gauthier and Weschke, Charlotte and van Nistelrooij, Niels and Verhelst, Pieter-Jan and Guo, Yan and Xu, Zhibin and Bienzeisler, Jonas and Rashad, Ashkan and Fl{\"u}gge, Tabea and Cotton, Ross and Vinayahalingam, Shankeeth and Ilesan, Robert and Raith, Stefan and Madsen, Dennis and Seibold, Constantin and Xi, Tong and Berg{\´e}, Stefaan and Nebelung, Sven and Kodym, Oldřich and Sundqvist, Osku and Thieringer, Florian and Lamecker, Hans and Coppens, Antoine and Potrusil, Thomas and Kraeima, Joep and Witjes, Max and Wu, Guomin and Chen, Xiaojun and Lambrechts, Adriaan and Cevidanes, Lucia H Soares and Zachow, Stefan and Hermans, Alexander and Truhn, Daniel and Alves, Victor and Egger, Jan and R{\"o}hrig, Rainer and H{\"o}lzle, Frank and Puladi, Behrus}, title = {Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems}, volume = {299}, journal = {Expert Systems With Applications}, number = {Part D}, doi = {10.1016/j.eswa.2025.130031}, year = {2025}, abstract = {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.}, language = {en} }