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 CB, accepted. https://github.com/INM-6/multi-area-model

Maximilian Schmidt e448763e23 gin commit from schmidt-T460p 5 years ago
08a3a1a88c19193b0af9d9d8f7a52344d1b17498 d9a4bd2cbf gin upload from schmidt-T460p 5 years ago
0adda4a542c3d5d43aebf7c30d876b6c5fd1d63e bcbe240051 gin upload from schmidt-T460p 5 years ago
1474e1884422b5b2096d3b7a20fd4bdf388af7e0 e448763e23 gin commit from schmidt-T460p 5 years ago
LICENSE.md 5fdbbbc639 Update README and add LICENSE file 5 years ago
README.md 6a900f5367 Add acknowledgements 5 years ago
model_construction.png 67e30a2eda Add model sketch 5 years ago

README.md

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 (accepted).

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 summarized the simulations and their most important parameters:

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