ArticleFrontiers in neuroinformatics2019
CoreNEURON : An Optimized Compute Engine for the NEURON Simulator.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
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- Computational modeling reveals biological mechanisms underlying the whisker flick EEG.iScience · 2025Article
- Ectopic sodium channel expression decreases excitability of Drosophila Kenyon cells.The Journal of physiology · 2025Article
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- Modeling nitric oxide diffusion and plasticity modulation in cerebellar learning.APL bioengineering · 2025Article
- BlueRecording: A pipeline for the efficient calculation of extracellular recordings in large-scale neural circuit models.PLoS computational biology · 2025Article
- Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes.iScience · 2025Article
- A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations.Network neuroscience (Cambridge, Mass.) · 2025Article
- Python/NEURON code for simulating biophysically realistic thalamocortical dynamics during sleep.Software impacts · 2024Article
- Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.Nature communications · 2024Article
- Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling.Briefings in bioinformatics · 2024Article
- Cortical cell assemblies and their underlying connectivity: An in silico study.PLoS computational biology · 2024Article
- Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA.Frontiers in neuroscience · 2024Article
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- Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories.PLoS computational biology · 2023Article
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7 authors.
Funding
Abstract
The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually measure in the millions of core hours. Supercomputer centers are transitioning to next generation architectures and the work accomplished per core hour for these simulations could be improved by an order of magnitude if NEURON was able to better utilize those new hardware capabilities. In order to adapt NEURON to evolving computer architectures, the compute engine of the NEURON simulator has been extracted and has been optimized as a library called CoreNEURON. This paper presents the design, implementation, and optimizations of CoreNEURON. We describe how CoreNEURON can be used as a library with NEURON and then compare performance of different network models on multiple architectures including IBM BlueGene/Q, Intel Skylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing NEURON network models with 4-7x less memory usage and 2-7x less execution time while maintaining binary result compatibility with NEURON.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.