Evidence map›Paper›PMID 34728257›Full record

ArticleJournal of neuroscience methods2022

NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs.

Roy Ben-Shalom, Alexander Ladd, Nikhil S Artherya, Christopher Cross, Kyung Geun Kim, Hersh Sanghevi, Alon Korngreen, Kristofer E Bouchard, Kevin J Bender

Open access · greenAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
5.0field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Review
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  8. Epilepsy-associatedbioRxiv : the preprint server for biology · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 2 countries.

Roy Ben-ShalomWeill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States; Department of Neurology, University of California, San Francisco, San Francisco, CA, United States; MIND Institute University of California, Davis, CA, United States; Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States. Electronic address: rbenshalom@ucdavis.edu.
Alexander LaddDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States.
Nikhil S ArtheryaDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States.
Christopher CrossWeill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States.
Kyung Geun KimDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States.
Hersh SangheviDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States.
Alon KorngreenThe Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
Kristofer E BouchardComputational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States; Hellen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States; Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, CA, United States.
Kevin J BenderWeill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States; Department of Neurology, University of California, San Francisco, San Francisco, CA, United States. Electronic address: kevin.bender@ucsf.edu.
University of California, Berkeley · USUniversity of California, San Francisco · USBar-Ilan University · ILLawrence Berkeley National Laboratory · USUniversity of California, Davis · US

Funding

Molecular and circuit mechanisms of antipsychoticsR01MH112729 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BENDER, KEVIN J, WHISTLER, JENNIFER L · 2017 to 2021
$2.6M
The function of SCN2A in neocortexR01MH125978 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BENDER, KEVIN J · 2021 to 2025
$2.0M
Cellular mechanisms for dopaminergic control of learning in prefrontal cortexR01DA035913 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BENDER, KEVIN J · 2013 to 2017
$1.7M
Towards a unified model of action potential generationF32NS095580 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BEN-SHALOM, ROY · 2015 to 2018
$172k
NIDA NIH HHS R01 DA035913NIMH NIH HHS R01 MH112729NIMH NIH HHS R01 MH125978NINDS NIH HHS F32 NS095580
6 · The paper itself

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.

Indexed as

AlgorithmsComputer GraphicsComputer SimulationMembrane PotentialsNeuronsBiophysical simulationsCompartmental modelsConductance-based modelsElectrophysiologyGraphical Processing Unit

Identifiers

PMID34728257
PMCPMC9887806
OpenAlexW3210095783

What OpenQuestion holds

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Registered trials

None linked

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.