Journal Article

FZJ-2025-03958

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Building on Models — A Perspective for Computational Neuroscience

Plesser, H. E.FZJ* ; Davison, A. P. ; Diesmann, M. (Corresponding author)FZJ* ; Fukai, T. ; Gemmeke, T. ; Gleeson, P. ; Knight, J. C. ; Nowotny, T. ; René, A. ; Rhodes, O. ; Roque, A. C. ; Senk, J.FZJ* ; Schwalger, T. ; Stadtmann, T. ; Tiddia, G. ; van Albada, S.FZJ*

2025
Oxford Univ. Press Oxford

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Please use a persistent id in citations: doi:10.1093/cercor/bhaf295  doi:10.34734/FZJ-2025-03958

Abstract: Neural circuit models are essential for integrating observations of the nervous system into a consistent whole. Public sharing of well-documented codes for such models facilitates further development. Nevertheless, scientific practice in computational neuroscience suffers from replication problems and little re-use of circuit models. One exception is a data-driven model of early sensory cortex by Potjans and Diesmann which has advanced computational neuroscience as a building block for more complex models. As a widely accepted benchmark for correctness and performance, the model has driven the development of CPU-based, GPU-based, and neuromorphic simulators. On the tenth anniversary of the publication of this model, experts convened at the Käte-Hamburger-Kolleg Cultures of Research at RWTH Aachen University to reflect on the reasons for the model’s success, its effect on computational neuroscience and technology development, and the perspectives this offers for the future of computational neuroscience. This report summarizes the observations by the workshop participants.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (IAS-6)
2. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 5232 - Computational Principles (POF4-523) (POF4-523)
2. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)
3. BMBF 01UK2104 - Käte Hamburger Kolleg "Kulturen des Forschens" (BMBF-01UK2104) (BMBF-01UK2104)
4. JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) (JL SMHB-2021-2027)
5. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)
6. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
7. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
8. EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) (101147319)
9. MULTIRULES - Synaptic multi-factor learning rules: from action potentials to behaviour (268689) (268689)
10. DFG project G:(GEPRIS)313856816 - SPP 2041: Computational Connectomics (313856816) (313856816)
11. HiRSE_PS - Helmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812) (HiRSE_PS-20220812)
12. ACA - Advanced Computing Architectures (SO-092) (SO-092)
13. BMBF 16ME0399 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0399) (BMBF-16ME0399)
14. BMBF 03ZU1106CA - NeuroSys: Algorithm-Hardware Co-Design (Projekt C) - A (03ZU1106CA) (03ZU1106CA)
15. RenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A) (BMBF-01IS19077A)
16. Brain-Scale Simulations (jinb33_20220812) (jinb33_20220812)
17. ICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858) (800858)

Appears in the scientific report 2025

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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