This title appears in the Scientific Report :
2020
Multi-area model of macaque cortex as a scaffold model and workflow testcase
Multi-area model of macaque cortex as a scaffold model and workflow testcase
Multi-area model of macaque cortex as a scaffold model and workflow test caseAnno Kurth, Alexander van Meegen, Aitor Morales-Gregorio, Jari Pronold, Agnes Korcsak-Gorzo, Hannah Vollenbröker, Rembrandt Bakker, Markus Diesmann and Sacha J van AlbadaThere are many open questions on the relationships be...
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Personal Name(s): | Kurth, Anno (Corresponding author) |
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Morales-Gregorio, Aitor / van Meegen, Alexander / Pronold, Jari / Korcsak-Gorzo, Agnes / Vollenbröker, Hannah / Bakker, Rembrandt / Diesmann, Markus / van Albada, Sacha | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2020
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Conference: | Human Brain Project Summit 2020, Athens (Greece), 2020-02-03 - 2020-02-06 |
Document Type: |
Poster |
Research Program: |
JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) Human Brain Project Specific Grant Agreement 2 Theory, modelling and simulation |
Publikationsportal JuSER |
Multi-area model of macaque cortex as a scaffold model and workflow test caseAnno Kurth, Alexander van Meegen, Aitor Morales-Gregorio, Jari Pronold, Agnes Korcsak-Gorzo, Hannah Vollenbröker, Rembrandt Bakker, Markus Diesmann and Sacha J van AlbadaThere are many open questions on the relationships between the structure, dynamics and function of the brain, especially from a multi-modal perspective bridging micro-, meso- and macroscopic scales. Large-scale point neuron network models of cortical areas and their interconnections, integrating vast bodies of anatomical data, provide researchers with tools to investigate these issues. In order to make reliable steps in understanding, we need to take an incremental approach to the design of the models, and ensure that they can be built on by others.Here we present a multi-area model (MAM) describing all 32 areas of the macaque vision-related cortex [1] that serves as a scaffold for relating brain structure to its dynamics and function on multiple scales. The model connectivity is determined by processing available anatomical data into a layer-resolved connectome [2] of macaque vision-related cortex. A spiking neural network with the specified connectivity is constructed using NEST [3] and simulated on a supercomputer to study resting-state activity.The model is being extended and refined in various directions: In one project, the motor-related cortical areas are being added, thereby enabling the study of visuo-motor integration in a unified, biologically realistic framework. Mechanisms of spatial attention are being implemented as a first step towards modeling visual processing. Moreover, ongoing work explores the possibility of endowing the anatomically based model with information processing capabilities through learning methods for spiking neural networks [4]. The methods devised to create the macaque model are further generalized to construct a model of the human visual cortex taking into account different neuron characteristics [5] and different anatomical constraints obtained via diffusion imaging [6].Finally, a fully digitized illustrative workflow is provided alongside the MAM to ensure reproducibility and enable re-use by the community. All code is available on GitHub. The tool Snakemake [7] provides a reproducible and user-friendly framework for the execution of the model. The workflow from the anatomical data to the simulation code, analysis and visualization can serve as an example for similar data-driven brain models.[1] Schmidt, M., Bakker, R., Shen, K., Bezgin, G., Diesmann, M., & van Albada, S. J. (2018). A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology, 14(10).[2] Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M., & van Albada, S. J. (2018). Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function, 223(3), 1409-1435.[3] Peyser, A. et al. (2017). NEST 2.14.0. Zenodo. 10.5281/zenodo.882971. [4] Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W. (2018). Long short-term memory and learning-to-learn in networks of spiking neurons. Advances in Neural Information Processing Systems, 787-797.[5] Teeter, C., et al. (2018). Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Communications, 9, 709.[6] Van Essen, D. C., et al. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.[7] Köster, J., & Rahmann, S. (2012). Snakemake—a scalable bioinformatics workflow engine. Bioinformatics, 28(19), 2520-2522. |