Edited and reviewed by: Michael Denker, Jülich Research Centre, Germany
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Neuroscience research generates vast amounts of data, requiring advanced computing resources for storage, management, analysis, and simulation (Glasser et al.,
Interdisciplinary strategies and collaborations to address issues related to data sharing, integration, and analysis.
Security and privacy challenges, such as ethical issues, regulation, and government policies, and the potential for harmful or accidental data breaches.
Reproducibility and transparency concerns in large-scale computing, including data sharing, standards, and best practices for data collection, analysis, and archival.
Novel strategies and algorithms to exploit large-scale computing architectures and cloud technologies in neuroscience, e.g., for simulation, analysis, and data presentation.
This Research Topic provides a broad overview of the current challenges and emerging solutions, offering guidance for improving the scalability, efficiency, and accessibility of computational tools in this field. Four papers have ultimately been included; each one deals with one currently challenging aspect of large-scale computing in neuroscience.
The four papers featured in this Research Topic explore these themes from different angles, presenting diverse strategies to advance data processing, simulation, and modeling in neuroscience. Two papers were Original Research papers, and two were Methods papers.
In the first Method paper,
Classifying neuron types from extracellular recordings is a cornerstone of neuroscience but remains constrained by traditional waveform-based methods. In the first Original Research paper,
Simulating biologically realistic brain models at scale is essential for advancing our understanding of neural dynamics. Despite their utility the current platforms often fall short in flexibility, scalability, and ease of use. In the second Original Research paper,
Finally, in the second Method paper,
The papers featured in this Research Topic demonstrate how advanced computational methods and algorithms could overcome some critical challenges in large-scale neuroscience research. At broader level, this Research Topics could serve as a means for addressing the demands of large-scale neuroscience and anticipate future directions in this research field will continue to evolve, focusing on developing interoperable, scalable, and energy-efficient computational tools.
TN: Writing – original draft, Writing – review & editing. MW: Writing – review & editing. DM: Writing – review & editing.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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