Routing Brain Traffic Through the Von Neumann Bottleneck: Parallel Sorting and Refactoring

Autor: Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel

Publikováno v: Frontiers in Neuroinformatics, Vol 15 (2022)

Generic simulation code for spiking neuronal networks spends the major part of the time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval betwee

Externí odkaz: https://doaj.org/article/472df1a0960b4ccbad77c0b42bf72f4c

Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

Autor: Robin Pauli, Philipp Weidel, Susanne Kunkel, Abigail Morrison

Publikováno v: Frontiers in Neuroinformatics, Vol 12 (2018)

Any modeler who has attempted to reproduce a spiking neural network model from its description in a paper has discovered what a painful endeavor this is. Even when all parameters appear to have been specified, which is rare, typically the initial att

Externí odkaz: https://doaj.org/article/4a5023a7708048dab7ab010c98c2dd81

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers

Autor: Jakob Jordan, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, Susanne Kunkel

Publikováno v: Frontiers in Neuroinformatics, Vol 12 (2018)

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and syna

Externí odkaz: https://doaj.org/article/defe98c8d1ef4bd7899ed4925bbed69d

Limits to the development of feed-forward structures in large recurrent neuronal networks

Autor: Susanne Kunkel, Markus eDiesmann, Abigail Morrison

Publikováno v: Frontiers in Computational Neuroscience, Vol 4 (2011)

Spike-timing dependent plasticity (STDP) has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite t

Externí odkaz: https://doaj.org/article/5400378b4f4c42bf874913b137cedfd1

Modular Supercomputing for Neuroscience

Autor: Susanne Kunkel, Thomas Lippert, Estela Suarez, Anne Küsters, Hans E. Plesser

Publikováno v: Cham : Springer International Publishing, Lecture Notes in Computer Science 12339, 63-80 (2021). doi:10.1007/978-3-030-82427-3_5
Brain-Inspired Computing / Amunts, Katrin (Editor) [https://orcid.org/0000-0001-5828-0867] ; Cham : Springer International Publishing, 2021, Chapter 5 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-030-82426-6=978-3-030-82427-3 ; doi:10.1007/978-3-030-82427-3
Brain-Inspired Computing / Amunts, Katrin (Editor) [https://orcid.org/0000-0001-5828-0867] ; Cham : Springer International Publishing, 2021, Chapter 5 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-030-82426-6=978-3-030-82427-3 ; doi:10.1007/978-3-030-82427-3BrainComp 2019-Workshop on Brain-Inspired Computing, Cetraro, Italy, 2019-07-15-2019-07-19
Lecture Notes in Computer Science ISBN: 9783030824266
BrainComp

The precise simulation of the human brain requires coupling different models in order to cover the different physiological and functional aspects of this extremely complex organ. Each of this brain models is implemented following specific mathematica

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7dd3e0d3d1c58b57aeabe40bc1ddb25
https://hdl.handle.net/2128/28352