ArticleJournal of neuroscience methods2022
NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs.
Article in Journal of neuroscience methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.Nature communications · 2024Article
- Molecular and cellular context influences SCN8A variant function.JCI insight · 2024Article
- Epilepsy-associated SCN2A (NaV1.2) variants exhibit diverse and complex functional properties.The Journal of general physiology · 2023Article
- Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories.PLoS computational biology · 2023Article
- A GPU-based computational framework that bridges neuron simulation and artificial intelligence.Nature communications · 2023Article
- Epilepsy-associatedbioRxiv : the preprint server for biology · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.Frontiers in neuroinformatics · 2022Article
- Brian2CUDA: Flexible and Efficient Simulation of Spiking Neural Network Models on GPUs.Frontiers in neuroinformatics · 2022Article
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundThe membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. NEW
methodHere, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. RESULTS & COMPARISON WITH EXISTING
methodsNeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data.
conclusionsThus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.
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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.