We use cookies to ensure you get the best experience on our website
Home Explore Help News
Register Sign In

Scheduled service maintenance on November 22

On Friday, November 22, 2024, between 06:00 CET and 18:00 CET, GIN services will undergo planned maintenance. Extended service interruptions should be expected. We will try to keep downtimes to a minimum, but recommend that users avoid critical tasks, large data uploads, or DOI requests during this time.

We apologize for any inconvenience.

albada
/
multi-area-model-data
forked from maximilian.schmidt/multi-area-model-data
1
0
Fork 0
Files
Branch: master
Branches Tags
multi-area-m...
/
README.md

README.md 4.2 KB
Permalink History Download

Multi-scale spiking network model of macaque visual cortex: Simulation data

This repository holds the simulation data presented in Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque cortex. PLOS Computational Biology 14(10), e1006359.

Model overview

The simulation code of the model and the figure scripts for the paper reside in a public github repository available under the following url: https://github.com/INM-6/multi-area-model.

Simulations in this repository

This repository contains data from 17 different simulations that are presented in the paper. They are identified by their unique hashed labels that are generated from the parameter sets used in the respective simulation.

Here is a table that summarizes the simulations and their most important parameters:

Connectivity matrix Coupling constant for cortico-cortical synaptic weights Increased external input onto population 5E and 6E Simulation time (ms) Figures in Schmidt et al. 2018 Simulation label
before stabilization 1. 1. 10500 2 533d73357f
before stabilization 1. 1.125 10500 2 0adda4a542
after stabilization 1. 1.125 10500 2,3,4,6,8 33fb595555
after stabilization 1.9 1.125 10500 5,6 3afaec94d6
after stabilization 1.9 1.125 100500 4,5,6,7,8,9 99c0024eac
after stabilization 1.4 1.125 10500 8 783cedb0ff
after stabilization 1.5 1.125 10500 8 380856f3b3
after stabilization 1.6 1.125 10500 8 5a7c6c2d6d
after stabilization 1.7 1.125 10500 8 c1876856b1
after stabilization 1.75 1.125 10500 8 a30f6fba65
after stabilization 1.8 1.125 10500 4,8 1474e18844
after stabilization 2. 1.125 10500 4,8 f18158895a
after stabilization 2.1 1.125 10500 4,8 08a3a1a88c
after stabilization 2.5 1.125 10500 4,6,8 5bdd72887b

To reduce the required space, we here refrain from publishing the original spiking data (~150 GB) and publish only the post-processed data presented in the figures of Schmidt et al.

Citation

If you use these data, we ask you to cite our paper in your publication. If you have questions regarding the code or scientific content, please create an issue on the simulation code repository on github: https://github.com/INM-6/multi-area-model.

Acknowledgements

This work was supported by the Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB), the European Union 7th Framework Program (Grant 269921, BrainScaleS and 604102, Human Brain Project, Ramp up phase) and European Unions Horizon 2020 research and innovation program (Grant 720270, Human Brain Project, SGA1), the Jülich Aachen Research Alliance (JARA), the Helmholtz young investigator group VH-NG-1028,and the German Research Council (DFG Grants SFB936/A1,Z1 and TRR169/A2) and computing time granted by the JARA-HPC Ver- gabegremium and provided on the JARA-HPC Partition part of the supercomputer JUQUEEN (Jülich Supercomputing Centre 2015) at Forschungszentrum Jülich (VSR Computation Time Grant JINB33), and Priority Program 2041 (SPP 2041) "Computational Connectomics" of the German Research Foundation (DFG).