activity
20182026
most citedConnectivity Concepts in Neuronal Network Modeling

52 citations · 182 across the 14 of their papers we have counts for

collaborators

15 papers

q-bio.NC2026

Rewiring of adult neural networks - learning from structural plasticity across multiple scales

Michael Fauth, Han Lu, Jannik Luboeinski +36

Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their topology…

cs.DC2026

Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies

Jan Vogelsang, Melissa Lober, Catherine Mia Schöfmann +7

Drawing on ideas from continuous integration, we present concepts of an automated benchmarking pipeline for high performance applications. Customization and collaboration have been…

cs.DC2025

Scalable Construction of Spiking Neural Networks using up to thousands of GPUs

Bruno Golosio, Gianmarco Tiddia, José Villamar +10

Diverse scientific and engineering research areas deal with discrete, time-stamped changes in large systems of interacting delay differential equations. Simulating such complex sys…

cs.NE2025★ 1 cited

A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks

James C. Knight, Johanna Senk, Thomas Nowotny

The majority of research in both training Artificial Neural Networks (ANNs) and modeling learning in biological brains focuses on synaptic plasticity, where learning equates to cha…

cs.PF2025★ 3 cited

Constructive community race: full-density spiking neural network model drives neuromorphic computing

Johanna Senk, Anno C. Kurth, Steve Furber +18

The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a mo…

cs.IR2024★ 3 cited

Metadata practices for simulation workflows

José Villamar, Matthias Kelbling, Heather L. More +4

Computer simulations are an essential pillar of knowledge generation in science. Exploring, understanding, reproducing, and sharing the results of simulations relies on tracking an…

q-bio.NC2023★ 10 cited

Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices

Bruno Golosio, Jose Villamar, Gianmarco Tiddia +6

Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also ho…

q-bio.NC2022★ 1 cited

Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

Agnes Korcsak-Gorzo, Charl Linssen, Jasper Albers +9

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for em…

q-bio.NC2021★ 16 cited

A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

Jasper Albers, Jari Pronold, Anno Christopher Kurth +9

Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with ad…

cs.DC2021★ 27 cited

Sub-realtime simulation of a neuronal network of natural density

Anno C. Kurth, Johanna Senk, Dennis Terhorst +2

Full scale simulations of neuronal network models of the brain are challenging due to the high density of connections between neurons. This contribution reports run times shorter t…

q-bio.NC2021★ 52 cited

Connectivity Concepts in Neuronal Network Modeling

Johanna Senk, Birgit Kriener, Mikael Djurfeldt +7

Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about…

q-bio.NC2021★ 13 cited

Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution

Stefan Dasbach, Tom Tetzlaff, Markus Diesmann +1

The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and N…

q-bio.NC2018★ 18 cited

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Johanna Senk, Espen Hagen, Sacha J. van Albada +1

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity…

q-bio.NC2018★ 12 cited

VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Johanna Senk, Corto Carde, Espen Hagen +3

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measur…

q-bio.NC2018★ 26 cited

Conditions for wave trains in spiking neural networks

Johanna Senk, Karolína Korvasová, Jannis Schuecker +4

Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unkn…