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| 001 | 1049767 | ||
| 005 | 20251229163326.0 | ||
| 037 | _ | _ | |a FZJ-2025-05551 |
| 100 | 1 | _ | |a Lu, Han |0 P:(DE-Juel1)204237 |b 0 |
| 111 | 2 | _ | |a ISC High Performance 2025 |g ISC25 |c Hamburg |d 2025-06-10 - 2025-06-13 |w Germany |
| 245 | _ | _ | |a Accelerating Brain Simulations using High Performance Computing |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a High-performance computing (HPC) has become an indispensable tool for scientific research, enabling the simulation, analysis, and visualization of complex systems across a wide range of disciplines. In neuroscience, HPC empowers researchers to explore the intricate mechanisms of brain functions, from single-cell dynamics to whole-brain network interactions. Our work leverages HPC to model neural dynamics across multiple scales, utilizing state-of-the-art simulation tools. At the microscale, we employ the Arbor simulator to investigate the dynamics of individual neurons, capturing detailed biophysical processes. At the mesoscale, we use the NEST simulator to model large-scale neural networks with synaptic plasticity, shedding light on mechanisms underlying learning and memory. At the macroscale, we utilize The Virtual Brain (TVB) platform to study whole-brain dynamics, providing insights into emergent brain processes by integrating structural and functional dynamics. The platform incorporates a GPU-accelerated framework to alleviate the computational burden associated with exploring large parameter spaces in brain simulations. To further advance our understanding of brain function, we have recently begun integrating these scales through co-simulation approaches. For instance, we have combined NEST and TVB to study interactions between large-scale network activity and whole-brain dynamics. Similarly, Arbor-TVB co-simulations enable us to bridge detailed cellular processes with system-level behavior. These multiscale models provide a more comprehensive understanding of the brain’s fundamental mechanisms, offering new perspectives on both healthy and pathological conditions such as epilepsy and Alzheimer’s disease. A substantial part of our work involves translating neuroscientific models into code that can be efficiently distributed on HPC systems. This requires a strong understanding of both the underlying neuroscience and parallel programming. By presenting our work at ISC 2025, we aim to advance the use of HPC in neuroscience and bring researchers together to tackle pressing challenges, such as memory constraints in large-scale brain simulations. |
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| 700 | 1 | _ | |a Fotiadou, Sinovia |0 P:(DE-Juel1)203512 |b 1 |
| 700 | 1 | _ | |a Frings, Maren |0 P:(DE-Juel1)172652 |b 2 |
| 700 | 1 | _ | |a Diaz, Sandra |0 P:(DE-Juel1)165859 |b 3 |
| 700 | 1 | _ | |a Hater, Thorsten |0 P:(DE-Juel1)176815 |b 4 |
| 700 | 1 | _ | |a Zossimova, Ekaterina |0 P:(DE-Juel1)204540 |b 5 |e Corresponding author |
| 700 | 1 | _ | |a van der Vlag, Michiel |0 P:(DE-Juel1)179447 |b 6 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1049767/files/ISC2025_poster_scientific_applications.pptx.pdf |y Restricted |
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