Spiking Neural Network Simulations

_{Copyright: Overview of a multi-area model of the vision-related areas of macaque cortex (Schmidt et al., 2018 PLoS Comput Biol & Schmidt et al., 2018 Brain Struct Func)}

There are many open questions concerning the links between the neuronal connectivity structure, dynamics, and function of the cerebral cortex. Two main complementary modeling approaches exist for addressing these questions: a bottom-up approach in which wide-ranging experimental data are brought together into coherent models; and a top-down approach in which hypotheses about network function and dynamics are implemented and their conformity with biological data tested and improved afterward. In our group, we mainly follow the former approach to develop large-scale spiking network models of primate and human cortices. However, this bottom-up approach is complemented with top-down approaches where useful (see section “Mean-Field Modeling and Theory”).

In Jülich, we are in the unique position of having supercomputational facilities as well as the simulation technology of NEST available for performing massive-scale simulations of mammalian cerebral cortex. In this endeavor, we focus primarily on the resting state as a ground state on which the functional dynamics of cortex unfolds. This is intended as a stepping-stone toward investigations of cortical information processing supported by the anatomical and dynamical substrate.

Publications

• Jiang H-J., Qi G., Duarte R., Feldmeyer D., van Albada SJ. (2024) A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity. Cerebral Cortex 34 (9), bhae378
  DOI: 10.1093/cercor/bhae378
  
  
• Pronold J., van Meegen A., Vollenbröker H., Shimoura R.O., Senden M., Hilgetag C.C., Bakker R., van Albada, S.J. (2024) Multi-Scale Spiking Network Model of Human Cerebral Cortex. Cerebral Cortex.
  DOI: 10.1093/cercor/bhae409
  
  
• Rostami V., Rost T., Schmitt F. J., van Albada S. J. , Riehle A., Nawrot M. P. (2024) Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information. Nature Communications, 15 (1), 6304. DOI: 10.1038/s41467-024-49889-4
  
  
• Senk J., Hagen E., van Albada S.J., Diesmann M. (2024) Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space. Cerebral Cortex. 34(10).
  DOI: 10.1093/cercor/bhae405
  
  
• Korcsak-Gorzo A., Müller MG., Baumbach A., Leng L., Breitwieser OJ., van Albada SJ., Senn W., Meier K., Legenstein R., Petrocivi MA. (2022) Cortical oscillations support sampling-based computations in spiking neural networks. PLoS Computational Biology 18(3):e1009753.
  DOI: 10.1371/journal.pcbi.1009753
  
  
• Tiddia G., Golosio B., Albers J., Senk J., Simula F., Pronold J., Fanti V., Pastorelli E., Pier Stanislao P., van Albada SJ. (2022) Fast Simulation of a Multi-Area Spiking Network Model of Macaque Cortex on an MPI-GPU Cluster. Frontiers in Neuroinformatics 16:883333.
  DOI: 10.3389/fninf.2022.883333
  
  
• Maksimov A., Diesmann M., van Albada SJ. (2018) Criteria on Balance, Stability and Excitability in Cortical Networks for Constraining Computational Models. Frontiers in Computational Neuroscience 12: 44.
  DOI: 10.3389/fncom.2018.00044
  
  
• Schmidt M., Bakker R., Shen K., Bezgin G., Diesmann M., van Albada SJ. (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS Computational Biology 14:e1006359.
  DOI: 10.1371/journal.pcbi.1006359
  
  
• Hagen E., Dahmen D., Stavrinou ML., Lindén H., Tetzlaff T., van Albada SJ., Grün S., Diesmann M., Einevoll GT. (2016) Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. Cerebral Cortex 26:4461–4496.
  DOI: 10.1093/cercor/bhw237.
  
  
• Maksimov A, van Albada SJ. , Diesmann M. (2016) [Re] Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model. Rescience.
  DOI:10.5281/zenodo.161526
  
  
• Kerr CC., van Albada SJ., Neymotin SA., Chadderdon GL., Robinson PA., Lytton WW. (2013) Cortical information flow in Parkinson’s disease: a composite network/field model. Frontiers in Computational Neuroscience 7:39.
  DOI: 10.3389/fncom.2013.00039
  
  

Book Chapters

• van Albada SJ., Pronold J., van Meegen A., Diesmann M. (2021) Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex. In: Amunts K., Grandinetti L., Lippert T., Petkov N. eds. Brain-Inspired Computing. Cham : Springer, Lecture Notes in Computer Science 12339, 47-59.
  DOI: 10.1007/978-3-030-82427-3_4
  
  
• Schmidt M., Diesmann M., van Albada SJ. (2018) Necessity and feasibility of large-scale neuronal network simulations. In: Lecture Notes of the 49th IFF Spring School “Physics of Life"
  
  
  
• van Albada SJ., Kunkel S., Morrison A., Diesmann M. (2014) Integrating Brain Structure and Dynamics on Supercomputers. In: Grandinetti L., Lippert T., Petkov N. eds. Brain-Inspired Computing LNCS 8603:22-32.
  DOI: 10.1007/978-3-319-12084-3_3

• Multi-area model of the vision-related areas of macaque cortex.
  

• Multi-area model of human cortex
  

• Model of the local circuitry of mouse somatosensory cortex

• Prof. Timo Dickscheid, INM-1, Forschungszentrum Jülich, Germany

• Dr. Sebastian Bludau, INM-1, Forschungszentrum Jülich, Germany

• Dr. Christian Schiffer, INM-1, Forschungszentrum Jülich, Germany

• Prof. Dirk Feldmeyer, INM-10, Forschungszentrum Jülich, Germany

• Dr. Guanxiao Qi, INM-10, Forschungszentrum Jülich, Germany

• Prof. Claus Hilgetag, UKE Hamburg, Germany

• Prof. Martin Nawrot, University of Cologne, Germany

• Dr. Vahid Rostami, University of Cologne, Germany

• Prof. Egidio d'Angelo, University of Pavia, Italy

• Dr. Espen Hagen, University of Oslo, Norway

• Dr. Mihai Petrovici, University of Bern, Switzerland

• Dr. Stefan Mihalas, Allen Institute, USA

• BMBF (Award ID 2424124) as part of the NSF/NIH/DOE/ANR/BMBF/BSF/NICT/AEI Collaborative Research in Computational Neuroscience Program (2025-2030)

• Horizon Europe Programme under the Specific Grant Agreement No. 101147319 (EBRAINS 2.0 Project) (2024-2026)

• European Union Horizon 2020 research and innovation program (Grant 945539, Human Brain Project, SGA3) (2020‒2023)

• “Cellular, connectional and molecular heterogeneity in a large-scale computational model of the human cerebral cortex“, Deutsche Forschungsgemeinschaft Grant in the Priority Program “Computational Connectomics” (SPP 2041) (2021‒2024)

• “Integrating multi-scale connectivity and brain architecture in a large-scale computational model of the human cerebral cortex”, Deutsche Forschungsgemeinschaft Grant in the Priority Program “Computational Connectomics” (SPP 2041) (2018‒2021)

• European Union Horizon 2020 research and innovation program (Grant 785907, Human Brain Project, SGA2) (2017‒2020)