@misc{21971,
  abstract     = {A Rust library for analyzing dendritic structures using quadric matrices. This project provides efficient tools for representing dendritic trees, computing quadric error metrics, and visualizing eigenvalue distributions on hexagonal plots.

This library implements quadric-based geometric analysis of dendritic structures, commonly found in neuroscience applications. Key features include:

Tree data structures: Hierarchical vertex and edge representations for dendritic trees
Quadric matrices: Computation of quadric error metrics for edges and vertices
Visualisation: Hexagonal plot generation using NormPolar transformations
Interactive tools: Desktop application with plotting capabilities},
  author       = {Bleile, Yossi and Cortinovis, Emanuele},
  keywords     = {quadratics, mathematics, dendrites, geometry, topology},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Quadrix}},
  doi          = {10.15479/AT-ISTA-21971},
  year         = {2026},
}

@misc{21442,
  author       = {Schlögl, Alois},
  keywords     = {hypocampus, ca3 simulations, modelling},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{CA3Simu v1.06 (vargas2026v1)}},
  doi          = {10.15479/AT-ISTA-21442},
  year         = {2026},
}

@misc{14926,
  author       = {Hauschild, Robert},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Matlab script for analysis of clone dispersal}},
  doi          = {10.15479/AT:ISTA:14926},
  year         = {2024},
}

@misc{14502,
  abstract     = {A precise quantitative description of the ultrastructural characteristics underlying biological mechanisms is often key to their understanding. This is particularly true for dynamic extra- and intracellular filamentous assemblies, playing a role in cell motility, cell integrity, cytokinesis, tissue formation and maintenance. For example, genetic manipulation or modulation of actin regulatory proteins frequently manifests in changes of the morphology, dynamics, and ultrastructural architecture of actin filament-rich cell peripheral structures, such as lamellipodia or filopodia. However, the observed ultrastructural effects often remain subtle and require sufficiently large datasets for appropriate quantitative analysis. The acquisition of such large datasets has been enabled by recent advances in high-throughput cryo-electron tomography (cryo-ET) methods. This also necessitates the development of complementary approaches to maximize the extraction of relevant biological information. We have developed a computational toolbox for the semi-automatic quantification of segmented and vectorized fila- mentous networks from pre-processed cryo-electron tomograms, facilitating the analysis and cross-comparison of multiple experimental conditions. GUI-based components simplify the processing of data and allow users to obtain a large number of ultrastructural parameters describing filamentous assemblies. We demonstrate the feasibility of this workflow by analyzing cryo-ET data of untreated and chemically perturbed branched actin filament networks and that of parallel actin filament arrays. In principle, the computational toolbox presented here is applicable for data analysis comprising any type of filaments in regular (i.e. parallel) or random arrangement. We show that it can ease the identification of key differences between experimental groups and facilitate the in-depth analysis of ultrastructural data in a time-efficient manner.},
  author       = {Dimchev, Georgi A and Amiri, Behnam and Fäßler, Florian and Falcke, Martin and Schur, Florian KM},
  keywords     = {cryo-electron tomography, actin cytoskeleton, toolbox},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Computational toolbox for ultrastructural quantitative analysis of filament networks in cryo-ET data}},
  doi          = {10.15479/AT:ISTA:14502},
  year         = {2023},
}

@misc{9327,
  abstract     = {This archive contains the missing sweater mesh animations and displacement models for the code of "Mechanics-Aware Deformation of Yarn Pattern Geometry"

Code Repository: https://git.ist.ac.at/gsperl/MADYPG},
  author       = {Sperl, Georg and Narain, Rahul and Wojtan, Christopher J},
  publisher    = {IST Austria},
  title        = {{Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data)}},
  doi          = {10.15479/AT:ISTA:9327},
  year         = {2021},
}

@misc{10110,
  abstract     = {Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks.},
  author       = {Guzmán, José and Schlögl, Alois and Espinoza Martinez, Claudia  and Zhang, Xiaomin and Suter, Benjamin and Jonas, Peter M},
  publisher    = {IST Austria},
  title        = {{How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network}},
  doi          = {10.15479/AT:ISTA:10110},
  year         = {2021},
}

@misc{8181,
  author       = {Hauschild, Robert},
  publisher    = {IST Austria},
  title        = {{Amplified centrosomes in dendritic cells promote immune cell effector functions}},
  doi          = {10.15479/AT:ISTA:8181},
  year         = {2020},
}

@misc{8294,
  abstract     = {Automated root growth analysis and tracking of root tips. },
  author       = {Hauschild, Robert},
  publisher    = {IST Austria},
  title        = {{RGtracker}},
  doi          = {10.15479/AT:ISTA:8294},
  year         = {2020},
}