ArticleNeural computation2016
Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.
Article in Neural computation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Multimodal optical imaging combining voltage-sensitive dye ElectroFluor630 with genetically encoded calcium, glutamate, or voltage indicators.Neurophotonics · 2026Article
- Self-organized and self-sustained ensemble activity patterns in simulation of mouse primary motor cortex.bioRxiv : the preprint server for biology · 2025Article
- Python/NEURON code for simulating biophysically realistic thalamocortical dynamics during sleep.Software impacts · 2024Article
- Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics.Cell reports · 2023Article
- A GPU-based computational framework that bridges neuron simulation and artificial intelligence.Nature communications · 2023Article
- Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics.The Journal of physiology · 2023Article
- Tutorial: using NEURON for neuromechanical simulations.Frontiers in computational neuroscience · 2023Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Modernizing the NEURON Simulator for Sustainability, Portability, and Performance.Frontiers in neuroinformatics · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.Frontiers in neuroinformatics · 2022Article
- NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model.Neural computation · 2021Article
- Event-Based Update of Synapses in Voltage-Based Learning Rules.Frontiers in neuroinformatics · 2021Article
- Article
- Continuing progress of spike sorting in the era of big data.Current opinion in neurobiology · 2019Review
- Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator.Frontiers in neuroinformatics · 2019Article
- Communication Sparsity in Distributed Spiking Neural Network Simulations to Improve Scalability.Frontiers in neuroinformatics · 2019Article
- Geppetto: a reusable modular open platform for exploring neuroscience data and models.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
- Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience.Frontiers in neuroinformatics · 2018Review
- Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.Frontiers in neuroinformatics · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.
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Registered trials
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.